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README.md

A Survey of Surveys (NLP & ML)

In this document, we survey hundreds of survey papers on Natural Language Processing (NLP) and Machine Learning (ML). We categorize these papers into popular topics and do simple counting for some interesting problems. In addition, we show the list of the papers with urls (813 papers).

Categorization

We follow the ACL and ICML submission guideline of recent years, covering a broad range of areas in NLP and ML. The categorization is as follows:

To reduce class imbalance, we separate some of the hot sub-topics from the original categorization of ACL and ICML submissions. E.g., Named Entity Recognition is a first-level area in our categorization because it is the focus of several surveys.

Statistics

We show the number of paper in each area in Figures 1-2.

Figure 1: # of papers in each NLP area.

Figure 2: # of papers in each ML area..

Also, we plot paper number as a function of publication year (see Figure 3).

Figure 3: # of papers vs publication year.

In addition, we generate word clouds to show hot topics in these surveys (see Figures 4-5).

Figure 4: The word cloud for NLP.

Figure 5: The word cloud for ML.

The NLP Paper List

Computational Social Science and Social Media

  1. A Comprehensive Survey on Community Detection with Deep Learning. arXiv 2021 paper bib

    Xing Su, Shan Xue, Fanzhen Liu, Jia Wu, Jian Yang, Chuan Zhou, Wenbin Hu, Cécile Paris, Surya Nepal, Di Jin, Quan Z. Sheng, Philip S. Yu

  2. A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities. ACM Comput. Surv. 2020 paper bib

    Xinyi Zhou, Reza Zafarani

  3. A Survey of Race, Racism, and Anti-Racism in NLP. ACL 2021 paper bib

    Anjalie Field, Su Lin Blodgett, Zeerak Waseem, Yulia Tsvetkov

  4. A Survey on Computational Propaganda Detection. IJCAI 2020 paper bib

    Giovanni Da San Martino, Stefano Cresci, Alberto Barrón-Cedeño, Seunghak Yu, Roberto Di Pietro, Preslav Nakov

  5. Computational Sociolinguistics: A Survey. Comput. Linguistics 2016 paper bib

    Dong Nguyen, A. Seza Dogruöz, Carolyn Penstein Rosé, Franciska de Jong

  6. Confronting Abusive Language Online: A Survey from the Ethical and Human Rights Perspective. J. Artif. Intell. Res. 2021 paper bib

    Svetlana Kiritchenko, Isar Nejadgholi, Kathleen C. Fraser

  7. From Symbols to Embeddings: A Tale of Two Representations in Computational Social Science. J. Soc. Comput. 2021 paper bib

    Huimin Chen, Cheng Yang, Xuanming Zhang, Zhiyuan Liu, Maosong Sun, Jianbin Jin

  8. Language (Technology) is Power: A Critical Survey of "Bias" in NLP. ACL 2020 paper bib

    Su Lin Blodgett, Solon Barocas, Hal Daumé III, Hanna M. Wallach

  9. Societal Biases in Language Generation: Progress and Challenges. ACL 2021 paper bib

    Emily Sheng, Kai-Wei Chang, Prem Natarajan, Nanyun Peng

  10. Tackling Online Abuse: A Survey of Automated Abuse Detection Methods. arXiv 2019 paper bib

    Pushkar Mishra, Helen Yannakoudakis, Ekaterina Shutova

  11. When do Word Embeddings Accurately Reflect Surveys on our Beliefs About People?. ACL 2020 paper bib

    Kenneth Joseph, Jonathan H. Morgan

Dialogue and Interactive Systems

  1. A Survey of Arabic Dialogues Understanding for Spontaneous Dialogues and Instant Message. IJNLC 2015 paper bib

    AbdelRahim A. Elmadany, Sherif M. Abdou, Mervat Gheith

  2. A Survey of Available Corpora For Building Data-Driven Dialogue Systems: The Journal Version. Dialogue Discourse 2018 paper bib

    Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau

  3. A Survey of Document Grounded Dialogue Systems (DGDS). arXiv 2020 paper bib

    Longxuan Ma, Wei-Nan Zhang, Mingda Li, Ting Liu

  4. A Survey of Natural Language Generation Techniques with a Focus on Dialogue Systems - Past, Present and Future Directions. arXiv 2019 paper bib

    Sashank Santhanam, Samira Shaikh

  5. A Survey on Dialog Management: Recent Advances and Challenges. arXiv 2020 paper bib

    Yinpei Dai, Huihua Yu, Yixuan Jiang, Chengguang Tang, Yongbin Li, Jian Sun

  6. A Survey on Dialogue Systems: Recent Advances and New Frontiers. SIGKDD Explor. 2017 paper bib

    Hongshen Chen, Xiaorui Liu, Dawei Yin, Jiliang Tang

  7. Advances in Multi-turn Dialogue Comprehension: A Survey. arXiv 2021 paper bib

    Zhuosheng Zhang, Hai Zhao

  8. Challenges in Building Intelligent Open-domain Dialog Systems. ACM Trans. Inf. Syst. 2020 paper bib

    Minlie Huang, Xiaoyan Zhu, Jianfeng Gao

  9. Conversational Machine Comprehension: a Literature Review. COLING 2020 paper bib

    Somil Gupta, Bhanu Pratap Singh Rawat, Hong Yu

  10. Neural Approaches to Conversational AI. ACL 2018 paper bib

    Jianfeng Gao, Michel Galley, Lihong Li

  11. Neural Approaches to Conversational AI: Question Answering, Task-oriented Dialogues and Social Chatbots. Now Foundations and Trends 2019 paper bib

    Jianfeng Gao, Michel Galley, Lihong Li

  12. POMDP-Based Statistical Spoken Dialog Systems: A Review. Proc. IEEE 2013 paper bib

    Steve J. Young, Milica Gasic, Blaise Thomson, Jason D. Williams

  13. Recent Advances and Challenges in Task-oriented Dialog System. arXiv 2020 paper bib

    Zheng Zhang, Ryuichi Takanobu, Minlie Huang, Xiaoyan Zhu

  14. Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey. arXiv 2021 paper bib

    Jinjie Ni, Tom Young, Vlad Pandelea, Fuzhao Xue, Vinay Adiga, Erik Cambria

  15. Utterance-level Dialogue Understanding: An Empirical Study. arXiv 2020 paper bib

    Deepanway Ghosal, Navonil Majumder, Rada Mihalcea, Soujanya Poria

  16. How to Evaluate Your Dialogue Models: A Review of Approaches. arXiv 2021 paper bib

    Xinmeng Li, Wansen Wu, Long Qin, Quanjun Yin

Generation

  1. A Survey of Knowledge-Enhanced Text Generation. arXiv 2020 paper bib

    Wenhao Yu, Chenguang Zhu, Zaitang Li, Zhiting Hu, Qingyun Wang, Heng Ji, Meng Jiang

  2. A Survey on Text Simplification. arXiv 2020 paper bib

    Punardeep Sikka, Vijay Mago

  3. Automatic Detection of Machine Generated Text: A Critical Survey. COLING 2020 paper bib

    Ganesh Jawahar, Muhammad Abdul-Mageed, Laks V. S. Lakshmanan

  4. Automatic Story Generation: Challenges and Attempts. arXiv 2021 paper bib

    Amal Alabdulkarim, Siyan Li, Xiangyu Peng

  5. Content Selection in Data-to-Text Systems: A Survey. arXiv 2016 paper bib

    Dimitra Gkatzia

  6. Data-Driven Sentence Simplification: Survey and Benchmark. Comput. Linguistics 2020 paper bib

    Fernando Alva-Manchego, Carolina Scarton, Lucia Specia

  7. Deep Learning for Text Style Transfer: A Survey. arXiv 2020 paper bib

    Di Jin, Zhijing Jin, Zhiting Hu, Olga Vechtomova, Rada Mihalcea

  8. Evaluation of Text Generation: A Survey. arXiv 2020 paper bib

    Asli Celikyilmaz, Elizabeth Clark, Jianfeng Gao

  9. Human Evaluation of Creative NLG Systems: An Interdisciplinary Survey on Recent Papers. arXiv 2021 paper bib

    Mika Hämäläinen, Khalid Al-Najjar

  10. Keyphrase Generation: A Multi-Aspect Survey. FRUCT 2019 paper bib

    Erion Çano, Ondrej Bojar

  11. Neural Language Generation: Formulation, Methods, and Evaluation. arXiv 2020 paper bib

    Cristina Garbacea, Qiaozhu Mei

  12. Neural Text Generation: Past, Present and Beyond. arXiv 2018 paper bib

    Sidi Lu, Yaoming Zhu, Weinan Zhang, Jun Wang, Yong Yu

  13. Quiz-Style Question Generation for News Stories. WWW 2021 paper bib

    Ádám D. Lelkes, Vinh Q. Tran, Cong Yu

  14. Recent Advances in Neural Question Generation. arXiv 2019 paper bib

    Liangming Pan, Wenqiang Lei, Tat-Seng Chua, Min-Yen Kan

  15. Recent Advances in SQL Query Generation: A Survey. arXiv 2020 paper bib

    Jovan Kalajdjieski, Martina Toshevska, Frosina Stojanovska

  16. Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation. J. Artif. Intell. Res. 2018 paper bib

    Albert Gatt, Emiel Krahmer

Information Extraction

  1. A Compact Survey on Event Extraction: Approaches and Applications. arXiv 2021 paper bib

    Qian Li, Hao Peng, Jianxin Li, Yiming Hei, Rui Sun, Jiawei Sheng, Shu Guo, Lihong Wang, Philip S. Yu

  2. A Review on Fact Extraction and Verification. arXiv 2020 paper bib

    Giannis Bekoulis, Christina Papagiannopoulou, Nikos Deligiannis

  3. A Survey of Deep Learning Methods for Relation Extraction. arXiv 2017 paper bib

    Shantanu Kumar

  4. A Survey of Event Extraction From Text. IEEE Access 2019 paper bib

    Wei Xiang, Bang Wang

  5. A Survey of event extraction methods from text for decision support systems. Decis. Support Syst. 2016 paper bib

    Frederik Hogenboom, Flavius Frasincar, Uzay Kaymak, Franciska de Jong, Emiel Caron

  6. A survey of joint intent detection and slot-filling models in natural language understanding. arXiv 2021 paper bib

    Henry Weld, Xiaoqi Huang, Siqi Long, Josiah Poon, Soyeon Caren Han

  7. A Survey of Textual Event Extraction from Social Networks. LPKM 2017 paper bib

    Mohamed Mejri, Jalel Akaichi

  8. A Survey on Open Information Extraction. COLING 2018 paper bib

    Christina Niklaus, Matthias Cetto, André Freitas, Siegfried Handschuh

  9. A Survey on Temporal Reasoning for Temporal Information Extraction from Text (Extended Abstract). IJCAI 2020 paper bib

    Artuur Leeuwenberg, Marie-Francine Moens

  10. An Overview of Event Extraction from Text. DeRiVE@ISWC 2011 paper bib

    Frederik Hogenboom, Flavius Frasincar, Uzay Kaymak, Franciska de Jong

  11. Automatic Extraction of Causal Relations from Natural Language Texts: A Comprehensive Survey. arXiv 2016 paper bib

    Nabiha Asghar

  12. Complex Relation Extraction: Challenges and Opportunities. arXiv 2020 paper bib

    Haiyun Jiang, Qiaoben Bao, Qiao Cheng, Deqing Yang, Li Wang, Yanghua Xiao

  13. Extracting Events and Their Relations from Texts: A Survey on Recent Research Progress and Challenges. AI Open 2020 paper bib

    Kang Liu, Yubo Chen, Jian Liu, Xinyu Zuo, Jun Zhao

  14. More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction. AACL 2020 paper bib

    Xu Han, Tianyu Gao, Yankai Lin, Hao Peng, Yaoliang Yang, Chaojun Xiao, Zhiyuan Liu, Peng Li, Jie Zhou, Maosong Sun

  15. Neural relation extraction: a survey. arXiv 2020 paper bib

    Mehmet Aydar, Ozge Bozal, Furkan Özbay

  16. Recent Neural Methods on Slot Filling and Intent Classification for Task-Oriented Dialogue Systems: A Survey. COLING 2020 paper bib

    Samuel Louvan, Bernardo Magnini

  17. Relation Extraction : A Survey. arXiv 2017 paper bib

    Sachin Pawar, Girish K. Palshikar, Pushpak Bhattacharyya

  18. Techniques for Jointly Extracting Entities and Relations: A Survey. arXiv 2021 paper bib

    Sachin Pawar, Pushpak Bhattacharyya, Girish K. Palshikar

Information Retrieval and Text Mining

  1. A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques. arXiv 2017 paper bib

    Mehdi Allahyari, Seyed Amin Pouriyeh, Mehdi Assefi, Saied Safaei, Elizabeth D. Trippe, Juan B. Gutierrez, Krys J. Kochut

  2. A survey of methods to ease the development of highly multilingual text mining applications. Lang. Resour. Evaluation 2012 paper bib

    Ralf Steinberger

  3. Data Mining and Information Retrieval in the 21st century: A bibliographic review. Comput. Sci. Rev. 2019 paper bib

    Jiaying Liu, Xiangjie Kong, Xinyu Zhou, Lei Wang, Da Zhang, Ivan Lee, Bo Xu, Feng Xia

  4. Neural Entity Linking: A Survey of Models Based on Deep Learning. arXiv 2020 paper bib

    Özge Sevgili, Artem Shelmanov, Mikhail Y. Arkhipov, Alexander Panchenko, Chris Biemann

  5. Neural Models for Information Retrieval. arXiv 2017 paper bib

    Bhaskar Mitra, Nick Craswell

  6. Opinion Mining and Analysis: A survey. IJNLC 2013 paper bib

    Arti Buche, M. B. Chandak, Akshay Zadgaonkar

  7. Relational World Knowledge Representation in Contextual Language Models: A Review. EMNLP 2021 paper bib

    Tara Safavi, Danai Koutra

  8. Short Text Topic Modeling Techniques, Applications, and Performance: A Survey. arXiv 2019 paper bib

    Jipeng Qiang, Zhenyu Qian, Yun Li, Yunhao Yuan, Xindong Wu

  9. Topic Modelling Meets Deep Neural Networks: A Survey. IJCAI 2021 paper bib

    He Zhao, Dinh Q. Phung, Viet Huynh, Yuan Jin, Lan Du, Wray L. Buntine

Interpretability and Analysis of Models for NLP

  1. A Primer in BERTology: What we know about how BERT works. Trans. Assoc. Comput. Linguistics 2020 paper bib

    Anna Rogers, Olga Kovaleva, Anna Rumshisky

  2. A Survey of the State of Explainable AI for Natural Language Processing. AACL 2020 paper bib

    Marina Danilevsky, Kun Qian, Ranit Aharonov, Yannis Katsis, Ban Kawas, Prithviraj Sen

  3. A Survey on Deep Learning and Explainability for Automatic Report Generation from Medical Images. arXiv 2020 paper bib

    Pablo Messina, Pablo Pino, Denis Parra, Alvaro Soto, Cecilia Besa, Sergio Uribe, Marcelo andía, Cristian Tejos, Claudia Prieto, Daniel Capurro

  4. A Survey on Explainability in Machine Reading Comprehension. arXiv 2020 paper bib

    Mokanarangan Thayaparan, Marco Valentino, André Freitas

  5. Analysis Methods in Neural Language Processing: A Survey. Trans. Assoc. Comput. Linguistics 2019 paper bib

    Yonatan Belinkov, James R. Glass

  6. Analyzing and Interpreting Neural Networks for NLP: A Report on the First BlackboxNLP Workshop. Nat. Lang. Eng. 2019 paper bib

    Afra Alishahi, Grzegorz Chrupala, Tal Linzen

  7. Post-hoc Interpretability for Neural NLP: A Survey. arXiv 2021 paper bib

    Andreas Madsen, Siva Reddy, Sarath Chandar

  8. Teach Me to Explain: A Review of Datasets for Explainable Natural Language Processing. arXiv 2021 paper bib

    Sarah Wiegreffe, Ana Marasović

  9. *Which BERT? A Survey Organizing Contextualized Encoders. EMNLP 2020 paper bib

    Patrick Xia, Shijie Wu, Benjamin Van Durme

Knowledge Graph

  1. A Review of Relational Machine Learning for Knowledge Graphs. Proc. IEEE 2016 paper bib

    Maximilian Nickel, Kevin Murphy, Volker Tresp, Evgeniy Gabrilovich

  2. A survey of embedding models of entities and relationships for knowledge graph completion. arXiv 2017 paper bib

    Dat Quoc Nguyen

  3. A Survey of Embedding Space Alignment Methods for Language and Knowledge Graphs. arXiv 2020 paper bib

    Alexander Kalinowski, Yuan An

  4. A Survey of Techniques for Constructing Chinese Knowledge Graphs and Their Applications. Sustainability 2018 paper bib

    Tianxing Wu, Guilin Qi, Cheng Li, Meng Wang

  5. A Survey on Graph Neural Networks for Knowledge Graph Completion. arXiv 2020 paper bib

    Siddhant Arora

  6. A Survey on Knowledge Graphs: Representation, Acquisition and Applications. arXiv 2020 paper bib

    Shaoxiong Ji, Shirui Pan, Erik Cambria, Pekka Marttinen, Philip S. Yu

  7. Introduction to neural network-based question answering over knowledge graphs. WIREs Data Mining Knowl. Discov. 2021 paper bib

    Nilesh Chakraborty, Denis Lukovnikov, Gaurav Maheshwari, Priyansh Trivedi, Jens Lehmann, Asja Fischer

  8. Knowledge Graph Embedding for Link Prediction: A Comparative Analysis. ACM Trans. Knowl. Discov. Data 2021 paper bib

    Andrea Rossi, Denilson Barbosa, Donatella Firmani, Antonio Matinata, Paolo Merialdo

  9. Knowledge Graph Embedding: A Survey of Approaches and Applications. IEEE Trans. Knowl. Data Eng. 2017 paper bib

    Quan Wang, Zhendong Mao, Bin Wang, Li Guo

  10. Knowledge Graph Refinement: A Survey of Approaches and Evaluation Methods. Semantic Web 2017 paper bib

    Heiko Paulheim

  11. Knowledge Graphs. ACM Comput. Surv. 2021 paper bib

    Aidan Hogan, Eva Blomqvist, Michael Cochez, Claudia d'Amato, Gerard de Melo, Claudio Gutiérrez, Sabrina Kirrane, José Emilio Labra Gayo, Roberto Navigli, Sebastian Neumaier, Axel-Cyrille Ngonga Ngomo, Axel Polleres, Sabbir M. Rashid, Anisa Rula, Lukas Schmelzeisen, Juan Sequeda, Steffen Staab, Antoine Zimmermann

  12. Knowledge Graphs: An Information Retrieval Perspective. Found. Trends Inf. Retr. 2020 paper bib

    Ridho Reinanda, Edgar Meij, Maarten de Rijke

  13. 知识表示学习研究进展. 计算机研究与发展 2016 paper bib

    刘知远, 孙茂松, 林衍凯, 谢若冰

  14. Neural, Symbolic and Neural-symbolic Reasoning on Knowledge Graphs. AI Open 2021 paper bib

    Jing Zhang, Bo Chen, Lingxi Zhang, Xirui Ke, Haipeng Ding

  15. Survey and Open Problems in Privacy Preserving Knowledge Graph: Merging, Query, Representation, Completion and Applications. arXiv 2020 paper bib

    Chaochao Chen, Jamie Cui, Guanfeng Liu, Jia Wu, Li Wang

  16. 领域知识图谱研究综述. 计算机系统应用 2020 paper bib

    刘烨宸, 李华昱

Language Grounding to Vision, Robotics and Beyond

  1. A comprehensive survey of mostly textual document segmentation algorithms since 2008. Pattern Recognit. 2017 paper bib

    Sébastien Eskenazi, Petra Gomez-Krämer, Jean-Marc Ogier

  2. Emotionally-Aware Chatbots: A Survey. arXiv 2019 paper bib

    Endang Wahyu Pamungkas

  3. From Show to Tell: A Survey on Deep Learning-based Image Captioning. arXiv 2021 paper bib

    Matteo Stefanini, Marcella Cornia, Lorenzo Baraldi, Silvia Cascianelli, Giuseppe Fiameni, Rita Cucchiara

  4. Trends in Integration of Vision and Language Research: A Survey of Tasks, Datasets, and Methods. arXiv 2019 paper bib

    Aditya Mogadala, Marimuthu Kalimuthu, Dietrich Klakow

Linguistic Theories, Cognitive Modeling and Psycholinguistics

  1. A Survey of Code-switching: Linguistic and Social Perspectives for Language Technologies. ACL 2021 paper bib

    A. Seza Dogruöz, Sunayana Sitaram, Barbara E. Bullock, Almeida Jacqueline Toribio

  2. Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing. Comput. Linguistics 2019 paper bib

    Edoardo Maria Ponti, Helen O'Horan, Yevgeni Berzak, Ivan Vulic, Roi Reichart, Thierry Poibeau, Ekaterina Shutova, Anna Korhonen

  3. Survey on the Use of Typological Information in Natural Language Processing. COLING 2016 paper bib

    Helen O'Horan, Yevgeni Berzak, Ivan Vulic, Roi Reichart, Anna Korhonen

Machine Learning for NLP

  1. A Comprehensive Survey on Word Representation Models: From Classical to State-Of-The-Art Word Representation Language Models. ACM Trans. Asian Low Resour. Lang. Inf. Process. 2021 paper bib

    Usman Naseem, Imran Razzak, Shah Khalid Khan, Mukesh Prasad

  2. A Survey Of Cross-lingual Word Embedding Models. J. Artif. Intell. Res. 2019 paper bib

    Sebastian Ruder, Ivan Vulic, Anders Søgaard

  3. A Survey of Data Augmentation Approaches for NLP. ACL 2021 paper bib

    Steven Y. Feng, Varun Gangal, Jason Wei, Sarath Chandar, Soroush Vosoughi, Teruko Mitamura, Eduard H. Hovy

  4. A Survey of Neural Network Techniques for Feature Extraction from Text. arXiv 2017 paper bib

    Vineet John

  5. A Survey of Neural Networks and Formal Languages. arXiv 2020 paper bib

    Joshua Ackerman, George Cybenko

  6. A Survey of the Usages of Deep Learning in Natural Language Processing. arXiv 2018 paper bib

    Daniel W. Otter, Julian R. Medina, Jugal K. Kalita

  7. A Survey on Contextual Embeddings. arXiv 2020 paper bib

    Qi Liu, Matt J. Kusner, Phil Blunsom

  8. A Survey on Transfer Learning in Natural Language Processing. arXiv 2020 paper bib

    Zaid Alyafeai, Maged Saeed AlShaibani, Irfan Ahmad

  9. Adversarial Attacks and Defense on Texts: A Survey. arXiv 2020 paper bib

    Aminul Huq, Mst. Tasnim Pervin

  10. Adversarial Attacks on Deep-Learning Models in Natural Language Processing: A Survey. ACM Trans. Intell. Syst. Technol. 2020 paper bib

    Wei Emma Zhang, Quan Z. Sheng, Ahoud Abdulrahmn F. Alhazmi, Chenliang Li

  11. An Empirical Survey of Unsupervised Text Representation Methods on Twitter Data. W-NUT@EMNLP 2020 paper bib

    Lili Wang, Chongyang Gao, Jason Wei, Weicheng Ma, Ruibo Liu, Soroush Vosoughi

  12. Bangla Natural Language Processing: A Comprehensive Review of Classical, Machine Learning, and Deep Learning Based Methods. arXiv 2021 paper bib

    Ovishake Sen, Mohtasim Fuad, Md. Nazrul Islam, Jakaria Rabbi, Md. Kamrul Hasan, Awal Ahmed Fime, Md. Tahmid Hasan Fuad, Delowar Sikder, Md. Akil Raihan Iftee

  13. Federated Learning Meets Natural Language Processing: A Survey. arXiv 2021 paper bib

    Ming Liu, Stella Ho, Mengqi Wang, Longxiang Gao, Yuan Jin, He Zhang

  14. From static to dynamic word representations: a survey. Int. J. Mach. Learn. Cybern. 2020 paper bib

    Yuxuan Wang, Yutai Hou, Wanxiang Che, Ting Liu

  15. From Word to Sense Embeddings: A Survey on Vector Representations of Meaning. J. Artif. Intell. Res. 2018 paper bib

    José Camacho-Collados, Mohammad Taher Pilehvar

  16. Graph Neural Networks for Natural Language Processing: A Survey. arXiv 2021 paper bib

    Lingfei Wu, Yu Chen, Kai Shen, Xiaojie Guo, Hanning Gao, Shucheng Li, Jian Pei, Bo Long

  17. Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systems. arXiv 2019 paper bib

    Laura von Rueden, Sebastian Mayer, Katharina Beckh, Bogdan Georgiev, Sven Giesselbach, Raoul Heese, Birgit Kirsch, Julius Pfrommer, Annika Pick, Rajkumar Ramamurthy, Michal Walczak, Jochen Garcke, Christian Bauckhage, Jannis Schuecker

  18. Narrative Science Systems: A Review. International Journal of Research in Computer Science 2015 paper bib

    Paramjot Kaur Sarao, Puneet Mittal, Rupinder Kaur

  19. Natural Language Processing Advancements By Deep Learning: A Survey. arXiv 2020 paper bib

    Amirsina Torfi, Rouzbeh A. Shirvani, Yaser Keneshloo, Nader Tavaf, Edward A. Fox

  20. Recent Trends in Deep Learning Based Natural Language Processing [Review Article]. IEEE Comput. Intell. Mag. 2018 paper bib

    Tom Young, Devamanyu Hazarika, Soujanya Poria, Erik Cambria

  21. 网络表示学习算法综述. 计算机科学 2020 paper bib

    丁钰, 魏浩, 潘志松, 刘鑫

  22. Symbolic, Distributed, and Distributional Representations for Natural Language Processing in the Era of Deep Learning: A Survey. Frontiers Robotics AI 2019 paper bib

    Lorenzo Ferrone, Fabio Massimo Zanzotto

  23. Token-Modification Adversarial Attacks for Natural Language Processing: A Survey. arXiv 2021 paper bib

    Tom Roth, Yansong Gao, Alsharif Abuadbba, Surya Nepal, Wei Liu

  24. Towards a Robust Deep Neural Network in Texts: A Survey. arXiv 2019 paper bib

    Wenqi Wang, Lina Wang, Run Wang, Zhibo Wang, Aoshuang Ye

  25. Word Embeddings: A Survey. arXiv 2019 paper bib

    Felipe Almeida, Geraldo Xexéo

Machine Translation

  1. A Comprehensive Survey of Multilingual Neural Machine Translation. arXiv 2020 paper bib

    Raj Dabre, Chenhui Chu, Anoop Kunchukuttan

  2. A Survey of Deep Learning Techniques for Neural Machine Translation. arXiv 2020 paper bib

    Shuoheng Yang, Yuxin Wang, Xiaowen Chu

  3. A Survey of Domain Adaptation for Neural Machine Translation. COLING 2018 paper bib

    Chenhui Chu, Rui Wang

  4. A Survey of Methods to Leverage Monolingual Data in Low-resource Neural Machine Translation. arXiv 2019 paper bib

    Ilshat Gibadullin, Aidar Valeev, Albina Khusainova, Adil Khan

  5. A Survey of Orthographic Information in Machine Translation. SN Comput. Sci. 2021 paper bib

    Bharathi Raja Chakravarthi, Priya Rani, Mihael Arcan, John P. McCrae

  6. A Survey of Word Reordering in Statistical Machine Translation: Computational Models and Language Phenomena. Comput. Linguistics 2016 paper bib

    Arianna Bisazza, Marcello Federico

  7. A Survey on Document-level Neural Machine Translation: Methods and Evaluation. ACM Comput. Surv. 2021 paper bib

    Sameen Maruf, Fahimeh Saleh, Gholamreza Haffari

  8. A Survey on Low-Resource Neural Machine Translation. IJCAI 2021 paper bib

    Rui Wang, Xu Tan, Renqian Luo, Tao Qin, Tie-Yan Liu

  9. Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Survey. arXiv 2021 paper bib

    Danielle Saunders

  10. Gender Bias in Machine Translation. arXiv 2021 paper bib

    Beatrice Savoldi, Marco Gaido, Luisa Bentivogli, Matteo Negri, Marco Turchi

  11. Machine Translation Approaches and Survey for Indian Languages. Int. J. Comput. Linguistics Chin. Lang. Process. 2013 paper bib

    P. J. Antony

  12. Machine Translation Approaches and Survey for Indian Languages. arXiv 2017 paper bib

    Nadeem Jadoon Khan, Waqas Anwar, Nadir Durrani

  13. Machine Translation Evaluation Resources and Methods: A Survey. Ireland Postgraduate Research Conference 2018 paper bib

    Lifeng Han

  14. Machine Translation using Semantic Web Technologies: A Survey. J. Web Semant. 2018 paper bib

    Diego Moussallem, Matthias Wauer, Axel-Cyrille Ngonga Ngomo

  15. Machine-Translation History and Evolution: Survey for Arabic-English Translations. CJAST 2017 paper bib

    Nabeel T. Alsohybe, Neama Abdulaziz Dahan, Fadl Mutaher Ba-Alwi

  16. Multimodal Machine Translation through Visuals and Speech. Mach. Transl. 2020 paper bib

    Umut Sulubacak, Ozan Caglayan, Stig-Arne Grönroos, Aku Rouhe, Desmond Elliott, Lucia Specia, Jörg Tiedemann

  17. Neural Machine Translation and Sequence-to-sequence Models: A Tutorial. arXiv 2017 paper bib

    Graham Neubig

  18. Neural Machine Translation for Low-Resource Languages: A Survey. arXiv 2021 paper bib

    Surangika Ranathunga, En-Shiun Annie Lee, Marjana Prifti Skenduli, Ravi Shekhar, Mehreen Alam, Rishemjit Kaur

  19. Neural Machine Translation: A Review. J. Artif. Intell. Res. 2020 paper bib

    Felix Stahlberg

  20. Neural machine translation: A review of methods, resources, and tools. AI Open 2020 paper bib

    Zhixing Tan, Shuo Wang, Zonghan Yang, Gang Chen, Xuancheng Huang, Maosong Sun, Yang Liu

  21. Neural Machine Translation: Challenges, Progress and Future. Science China Technological Sciences 2020 paper bib

    Jiajun Zhang, Chengqing Zong

  22. Survey of Low-Resource Machine Translation. arXiv 2021 paper bib

    Barry Haddow, Rachel Bawden, Antonio Valerio Miceli Barone, Jindrich Helcl, Alexandra Birch

  23. The Query Translation Landscape: a Survey. arXiv 2019 paper bib

    Mohamed Nadjib Mami, Damien Graux, Harsh Thakkar, Simon Scerri, Sören Auer, Jens Lehmann

  24. 神经机器翻译前沿综述. 中文信息学报 2020 paper bib

    冯洋, 邵晨泽

Named Entity Recognition

  1. A Survey of Arabic Named Entity Recognition and Classification. Comput. Linguistics 2014 paper bib

    Khaled Shaalan

  2. A survey of named entity recognition and classification. Lingvisticae Investigationes 2007 paper bib

    David Nadeau, Satoshi Sekine

  3. A Survey of Named Entity Recognition in Assamese and other Indian Languages. arXiv 2014 paper bib

    Gitimoni Talukdar, Pranjal Protim Borah, Arup Baruah

  4. A Survey on Deep Learning for Named Entity Recognition. IEEE Trans. Knowl. Data Eng. 2022 paper bib

    Jing Li, Aixin Sun, Jianglei Han, Chenliang Li

  5. A Survey on Recent Advances in Named Entity Recognition from Deep Learning models. COLING 2018 paper bib

    Vikas Yadav, Steven Bethard

  6. Design Challenges and Misconceptions in Neural Sequence Labeling. COLING 2018 paper bib

    Jie Yang, Shuailong Liang, Yue Zhang

Natural Language Inference

  1. A Comparative Survey of Recent Natural Language Interfaces for Databases. VLDB J. 2019 paper bib

    Katrin Affolter, Kurt Stockinger, Abraham Bernstein

  2. Beyond Leaderboards: A survey of methods for revealing weaknesses in Natural Language Inference data and models. arXiv 2020 paper bib

    Viktor Schlegel, Goran Nenadic, Riza Batista-Navarro

  3. Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches. arXiv 2019 paper bib

    Shane Storks, Qiaozi Gao, Joyce Y Chai

Natural Language Processing

  1. A bit of progress in language modeling. Comput. Speech Lang. 2001 paper bib

    Joshua T. Goodman

  2. A Brief Survey and Comparative Study of Recent Development of Pronoun Coreference Resolution. arXiv 2020 paper bib

    Hongming Zhang, Xinran Zhao, Yangqiu Song

  3. A Comprehensive Survey of Grammar Error Correction. arXiv 2020 paper bib

    Yu Wang, Yuelin Wang, Jie Liu, Zhuo Liu

  4. A Neural Entity Coreference Resolution Review. Expert Syst. Appl. 2021 paper bib

    Nikolaos Stylianou, Ioannis P. Vlahavas

  5. A Primer on Neural Network Models for Natural Language Processing. J. Artif. Intell. Res. 2016 paper bib

    Yoav Goldberg

  6. A Review of Bangla Natural Language Processing Tasks and the Utility of Transformer Models. arXiv 2021 paper bib

    Firoj Alam, Md. Arid Hasan, Tanvirul Alam, Akib Khan, Jannatul Tajrin, Naira Khan, Shammur Absar Chowdhury

  7. A Survey and Classification of Controlled Natural Languages. Comput. Linguistics 2014 paper bib

    Tobias Kuhn

  8. A Survey on Neural Network Language Models. arXiv 2019 paper bib

    Kun Jing, Jungang Xu

  9. A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios. NAACL-HLT 2021 paper bib

    Michael A. Hedderich, Lukas Lange, Heike Adel, Jannik Strötgen, Dietrich Klakow

  10. An Introductory Survey on Attention Mechanisms in NLP Problems. IntelliSys 2019 paper bib

    Dichao Hu

  11. Attention in Natural Language Processing. IEEE Trans. Neural Networks Learn. Syst. 2021 paper bib

    Andrea Galassi, Marco Lippi, Paolo Torroni

  12. Automatic Arabic Dialect Identification Systems for Written Texts: A Survey. arXiv 2020 paper bib

    Maha J. Althobaiti

  13. Chinese Word Segmentation: A Decade Review. Journal of Chinese Information Processing 2007 paper bib

    Changning Huang, Hai Zhao

  14. Continual Lifelong Learning in Natural Language Processing: A Survey. COLING 2020 paper bib

    Magdalena Biesialska, Katarzyna Biesialska, Marta R. Costa-jussà

  15. Experience Grounds Language. EMNLP 2020 paper bib

    Yonatan Bisk, Ari Holtzman, Jesse Thomason, Jacob Andreas, Yoshua Bengio, Joyce Chai, Mirella Lapata, Angeliki Lazaridou, Jonathan May, Aleksandr Nisnevich, Nicolas Pinto, Joseph P. Turian

  16. How Commonsense Knowledge Helps with Natural Language Tasks: A Survey of Recent Resources and Methodologies. arXiv 2021 paper bib

    Yubo Xie, Pearl Pu

  17. Jumping NLP curves: A review of natural language processing research [Review Article]. IEEE Comput. Intell. Mag. 2014 paper bib

    Erik Cambria, Bebo White

  18. Natural Language Processing - A Survey. arXiv 2012 paper bib

    Kevin Mote

  19. Natural Language Processing: State of The Art, Current Trends and Challenges. arXiv 2017 paper bib

    Diksha Khurana, Aditya Koli, Kiran Khatter, Sukhdev Singh

  20. Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question Answering. COLING 2018 paper bib

    Wuwei Lan, Wei Xu

  21. Overview of the Transformer-based Models for NLP Tasks. FedCSIS 2020 paper bib

    Anthony Gillioz, Jacky Casas, Elena Mugellini, Omar Abou Khaled

  22. Progress in Neural NLP: Modeling, Learning, and Reasoning. Engineering 2020 paper bib

    Ming Zhou, Nan Duan, Shujie Liu, Heung-Yeung Shum

  23. Putting Humans in the Natural Language Processing Loop: A Survey. arXiv 2021 paper bib

    Zijie J. Wang, Dongjin Choi, Shenyu Xu, Diyi Yang

  24. Survey on Publicly Available Sinhala Natural Language Processing Tools and Research. arXiv 2019 paper bib

    Nisansa de Silva

  25. Visualizing Natural Language Descriptions: A Survey. ACM Comput. Surv. 2016 paper bib

    Kaveh Hassani, Won-Sook Lee

NLP Applications

  1. A Short Survey of Biomedical Relation Extraction Techniques. arXiv 2017 paper bib

    Elham Shahab

  2. A survey on natural language processing (nlp) and applications in insurance. arXiv 2020 paper bib

    Antoine Ly, Benno Uthayasooriyar, Tingting Wang

  3. Android Security using NLP Techniques: A Review. arXiv 2021 paper bib

    Sevil Sen, Burcu Can

  4. Disinformation Detection: A review of linguistic feature selection and classification models in news veracity assessments. arXiv 2019 paper bib

    Jillian Tompkins

  5. Extraction and Analysis of Fictional Character Networks: A Survey. ACM Comput. Surv. 2019 paper bib

    Vincent Labatut, Xavier Bost

  6. How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence. ACL 2020 paper bib

    Haoxi Zhong, Chaojun Xiao, Cunchao Tu, Tianyang Zhang, Zhiyuan Liu, Maosong Sun

  7. Natural Language Based Financial Forecasting: A Survey. Artif. Intell. Rev. 2018 paper bib

    Frank Z. Xing, Erik Cambria, Roy E. Welsch

  8. Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Review. arXiv 2021 paper bib

    Irene Li, Jessica Pan, Jeremy Goldwasser, Neha Verma, Wai Pan Wong, Muhammed Yavuz Nuzumlali, Benjamin Rosand, Yixin Li, Matthew Zhang, David Chang, Richard Andrew Taylor, Harlan M. Krumholz, Dragomir R. Radev

  9. SECNLP: A survey of embeddings in clinical natural language processing. J. Biomed. Informatics 2020 paper bib

    Katikapalli Subramanyam Kalyan, Sivanesan Sangeetha

  10. Survey of Natural Language Processing Techniques in Bioinformatics. Comput. Math. Methods Medicine 2015 paper bib

    Zhiqiang Zeng, Hua Shi, Yun Wu, Zhiling Hong

  11. Survey of Text-based Epidemic Intelligence: A Computational Linguistics Perspective. ACM Comput. Surv. 2020 paper bib

    Aditya Joshi, Sarvnaz Karimi, Ross Sparks, Cécile Paris, C. Raina MacIntyre

  12. The Potential of Machine Learning and NLP for Handling Students' Feedback (A Short Survey). arXiv 2020 paper bib

    Maryam Edalati

  13. Towards Improved Model Design for Authorship Identification: A Survey on Writing Style Understanding. arXiv 2020 paper bib

    Weicheng Ma, Ruibo Liu, Lili Wang, Soroush Vosoughi

Pre-training

  1. A Primer on Contrastive Pretraining in Language Processing: Methods, Lessons Learned and Perspectives. arXiv 2021 paper bib

    Nils Rethmeier, Isabelle Augenstein

  2. A Short Survey of Pre-trained Language Models for Conversational AI-A NewAge in NLP. arXiv 2021 paper bib

    Munazza Zaib, Quan Z. Sheng, Wei Emma Zhang

  3. AMMUS : A Survey of Transformer-based Pretrained Models in Natural Language Processing. arXiv 2021 paper bib

    Katikapalli Subramanyam Kalyan, Ajit Rajasekharan, Sivanesan Sangeetha

  4. Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing. arXiv 2021 paper bib

    Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, Graham Neubig

  5. Pretrained Language Models for Text Generation: A Survey. arXiv 2021 paper bib

    Junyi Li, Tianyi Tang, Wayne Xin Zhao, Ji-Rong Wen

  6. Pre-trained models for natural language processing: A survey. Science China Technological Sciences 2020 paper bib

    Xipeng Qiu, Tianxiang Sun, Yige Xu, Yunfan Shao, Ning Dai, Xuanjing Huang

  7. Pre-Trained Models: Past, Present and Future. arXiv 2021 paper bib

    Xu Han, Zhengyan Zhang, Ning Ding, Yuxian Gu, Xiao Liu, Yuqi Huo, Jiezhong Qiu, Liang Zhang, Wentao Han, Minlie Huang, Qin Jin, Yanyan Lan, Yang Liu, Zhiyuan Liu, Zhiwu Lu, Xipeng Qiu, Ruihua Song, Jie Tang, Ji-Rong Wen, Jinhui Yuan, Wayne Xin Zhao, Jun Zhu

  8. Pretrained Transformers for Text Ranking: BERT and Beyond. WSDM 2021 paper bib

    Andrew Yates, Rodrigo Nogueira, Jimmy Lin

Question Answering

  1. A Survey of Question Answering over Knowledge Base. CCKS 2019 paper bib

    Peiyun Wu, Xiaowang Zhang, Zhiyong Feng

  2. A Survey on Complex Knowledge Base Question Answering: Methods, Challenges and Solutions. IJCAI 2021 paper bib

    Yunshi Lan, Gaole He, Jinhao Jiang, Jing Jiang, Wayne Xin Zhao, Ji-Rong Wen

  3. A Survey on Complex Question Answering over Knowledge Base: Recent Advances and Challenges. arXiv 2020 paper bib

    Bin Fu, Yunqi Qiu, Chengguang Tang, Yang Li, Haiyang Yu, Jian Sun

  4. A survey on question answering technology from an information retrieval perspective. Inf. Sci. 2011 paper bib

    Oleksandr Kolomiyets, Marie-Francine Moens

  5. A Survey on Why-Type Question Answering Systems. arXiv 2019 paper bib

    Manvi Breja, Sanjay Kumar Jain

  6. Complex Knowledge Base Question Answering: A Survey. arXiv 2021 paper bib

    Yunshi Lan, Gaole He, Jinhao Jiang, Jing Jiang, Wayne Xin Zhao, Ji-Rong Wen

  7. Core techniques of question answering systems over knowledge bases: a survey. Knowl. Inf. Syst. 2018 paper bib

    Dennis Diefenbach, Vanessa López, Kamal Deep Singh, Pierre Maret

  8. Introduction to Neural Network based Approaches for Question Answering over Knowledge Graphs. arXiv 2019 paper bib

    Nilesh Chakraborty, Denis Lukovnikov, Gaurav Maheshwari, Priyansh Trivedi, Jens Lehmann, Asja Fischer

  9. Narrative Question Answering with Cutting-Edge Open-Domain QA Techniques: A Comprehensive Study. arXiv 2021 paper bib

    Xiangyang Mou, Chenghao Yang, Mo Yu, Bingsheng Yao, Xiaoxiao Guo, Saloni Potdar, Hui Su

  10. Question Answering Systems: Survey and Trends. Procedia Computer Science 2015 paper bib

    Abdelghani Bouziane, Djelloul Bouchiha, Noureddine Doumi, Mimoun Malki

  11. Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering. arXiv 2021 paper bib

    Fengbin Zhu, Wenqiang Lei, Chao Wang, Jianming Zheng, Soujanya Poria, Tat-Seng Chua

  12. Survey of Visual Question Answering: Datasets and Techniques. arXiv 2017 paper bib

    Akshay Kumar Gupta

  13. Text-based Question Answering from Information Retrieval and Deep Neural Network Perspectives: A Survey. WIREs Data Mining Knowl. Discov. 2021 paper bib

    Zahra Abbasiantaeb, Saeedeh Momtazi

  14. Tutorial on Answering Questions about Images with Deep Learning. arXiv 2016 paper bib

    Mateusz Malinowski, Mario Fritz

  15. Visual Question Answering using Deep Learning: A Survey and Performance Analysis. CVIP 2020 paper bib

    Yash Srivastava, Vaishnav Murali, Shiv Ram Dubey, Snehasis Mukherjee

Reading Comprehension

  1. A Survey on Explainability in Machine Reading Comprehension. arXiv 2020 paper bib

    Mokanarangan Thayaparan, Marco Valentino, André Freitas

  2. A Survey on Machine Reading Comprehension Systems. arXiv 2020 paper bib

    Razieh Baradaran, Razieh Ghiasi, Hossein Amirkhani

  3. A Survey on Machine Reading Comprehension—Tasks, Evaluation Metrics and Benchmark Datasets. Applied Sciences 2020 paper bib

    Chengchang Zeng, Shaobo Li, Qin Li, Jie Hu, Jianjun Hu

  4. A Survey on Neural Machine Reading Comprehension. arXiv 2019 paper bib

    Boyu Qiu, Xu Chen, Jungang Xu, Yingfei Sun

  5. English Machine Reading Comprehension Datasets: A Survey. EMNLP 2021 paper bib

    Daria Dzendzik, Jennifer Foster, Carl Vogel

  6. Machine Reading Comprehension: a Literature Review. arXiv 2019 paper bib

    Xin Zhang, An Yang, Sujian Li, Yizhong Wang

  7. Machine Reading Comprehension: The Role of Contextualized Language Models and Beyond. arXiv 2020 paper bib

    Zhuosheng Zhang, Hai Zhao, Rui Wang

  8. Neural Machine Reading Comprehension: Methods and Trends. Applied Surface Science 2019 paper bib

    Shanshan Liu, Xin Zhang, Sheng Zhang, Hui Wang, Weiming Zhang

Recommender Systems

  1. A review on deep learning for recommender systems: challenges and remedies. Artif. Intell. Rev. 2019 paper bib

    Zeynep Batmaz, Ali Yürekli, Alper Bilge, Cihan Kaleli

  2. A Survey of Explanations in Recommender Systems. ICDE Workshops 2007 paper bib

    Nava Tintarev, Judith Masthoff

  3. A survey on Adversarial Recommender Systems: from Attack/Defense strategies to Generative Adversarial Networks. ACM Comput. Surv. 2021 paper bib

    Yashar Deldjoo, Tommaso Di Noia, Felice Antonio Merra

  4. A Survey on Conversational Recommender Systems. ACM Comput. Surv. 2021 paper bib

    Dietmar Jannach, Ahtsham Manzoor, Wanling Cai, Li Chen

  5. A Survey on Knowledge Graph-Based Recommender Systems. arXiv 2020 paper bib

    Qingyu Guo, Fuzhen Zhuang, Chuan Qin, Hengshu Zhu, Xing Xie, Hui Xiong, Qing He

  6. A Survey on Personality-Aware Recommendation Systems. arXiv 2021 paper bib

    Sahraoui Dhelim, Nyothiri Aung, Mohammed Amine Bouras, Huansheng Ning, Erik Cambria

  7. A Survey on Session-based Recommender Systems. ACM Comput. Surv. 2022 paper bib

    Shoujin Wang, Longbing Cao, Yan Wang, Quan Z. Sheng, Mehmet A. Orgun, Defu Lian

  8. Advances and Challenges in Conversational Recommender Systems: A Survey. arXiv 2021 paper bib

    Chongming Gao, Wenqiang Lei, Xiangnan He, Maarten de Rijke, Tat-Seng Chua

  9. Are we really making much progress? A worrying analysis of recent neural recommendation approaches. RecSys 2019 paper bib

    Maurizio Ferrari Dacrema, Paolo Cremonesi, Dietmar Jannach

  10. Bias and Debias in Recommender System: A Survey and Future Directions. arXiv 2020 paper bib

    Jiawei Chen, Hande Dong, Xiang Wang, Fuli Feng, Meng Wang, Xiangnan He

  11. Content-based Recommender Systems: State of the Art and Trends. Recommender Systems Handbook 2011 paper bib

    Pasquale Lops, Marco de Gemmis, Giovanni Semeraro

  12. Cross Domain Recommender Systems: A Systematic Literature Review. ACM Comput. Surv. 2017 paper bib

    Muhammad Murad Khan, Roliana Ibrahim, Imran Ghani

  13. Deep Conversational Recommender Systems: A New Frontier for Goal-Oriented Dialogue Systems. arXiv 2020 paper bib

    Dai Hoang Tran, Quan Z. Sheng, Wei Emma Zhang, Salma Abdalla Hamad, Munazza Zaib, Nguyen H. Tran, Lina Yao, Nguyen Lu Dang Khoa

  14. Deep Learning based Recommender System: A Survey and New Perspectives. ACM Comput. Surv. 2019 paper bib

    Shuai Zhang, Lina Yao, Aixin Sun, Yi Tay

  15. Deep Learning for Matching in Search and Recommendation. Found. Trends Inf. Retr. 2020 paper bib

    Jun Xu, Xiangnan He, Hang Li

  16. Deep Learning on Knowledge Graph for Recommender System: A Survey. arXiv 2020 paper bib

    Yang Gao, Yi-Fan Li, Yu Lin, Hang Gao, Latifur Khan

  17. Diversity in recommender systems – A survey. Knowledge-Based Systems 2017 paper bib

    Matevž Kunavera, Tomaž Požrl

  18. Explainable Recommendation: A Survey and New Perspectives. Found. Trends Inf. Retr. 2020 paper bib

    Yongfeng Zhang, Xu Chen

  19. Graph Learning based Recommender Systems: A Review. IJCAI 2021 paper bib

    Shoujin Wang, Liang Hu, Yan Wang, Xiangnan He, Quan Z. Sheng, Mehmet A. Orgun, Longbing Cao, Francesco Ricci, Philip S. Yu

  20. Graph Neural Networks in Recommender Systems: A Survey. arXiv 2020 paper bib

    Shiwen Wu, Wentao Zhang, Fei Sun, Bin Cui

  21. Hybrid Recommender Systems: Survey and Experiments. User Model. User Adapt. Interact. 2002 paper bib

    Robin D. Burke

  22. Knowledge Transfer via Pre-training for Recommendation: A Review and Prospect. Frontiers Big Data 2021 paper bib

    Zheni Zeng, Chaojun Xiao, Yuan Yao, Ruobing Xie, Zhiyuan Liu, Fen Lin, Leyu Lin, Maosong Sun

  23. Recommender systems survey. Knowledge-Based Systems 2013 paper bib

    Bobadilla J., Ortega F., Hernando A., Gutiérrez A.

  24. Sequence-Aware Recommender Systems. ACM Comput. Surv. 2018 paper bib

    Massimo Quadrana, Paolo Cremonesi, Dietmar Jannach

  25. Survey for Trust-aware Recommender Systems: A Deep Learning Perspective. arXiv 2020 paper bib

    Manqing Dong, Feng Yuan, Lina Yao, Xianzhi Wang, Xiwei Xu, Liming Zhu

  26. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 2005 paper bib

    Gediminas Adomavicius, Alexander Tuzhilin

  27. Use of Deep Learning in Modern Recommendation System: A Summary of Recent Works. arXiv 2017 paper bib

    Ayush Singhal, Pradeep Sinha, Rakesh Pant

Resources and Evaluation

  1. A Review of Human Evaluation for Style Transfer. arXiv 2021 paper bib

    Eleftheria Briakou, Sweta Agrawal, Ke Zhang, Joel R. Tetreault, Marine Carpuat

  2. A Short Survey on Sense-Annotated Corpora. LREC 2020 paper bib

    Tommaso Pasini, José Camacho-Collados

  3. A Survey of Current Datasets for Vision and Language Research. EMNLP 2015 paper bib

    Francis Ferraro, Nasrin Mostafazadeh, Ting-Hao (Kenneth) Huang, Lucy Vanderwende, Jacob Devlin, Michel Galley, Margaret Mitchell

  4. A Survey of Evaluation Metrics Used for NLG Systems. arXiv 2020 paper bib

    Ananya B. Sai, Akash Kumar Mohankumar, Mitesh M. Khapra

  5. A Survey of Word Embeddings Evaluation Methods. arXiv 2018 paper bib

    Amir Bakarov

  6. A Survey on Recognizing Textual Entailment as an NLP Evaluation. arXiv 2020 paper bib

    Adam Poliak

  7. Corpora Annotated with Negation: An Overview. Comput. Linguistics 2020 paper bib

    Salud María Jiménez Zafra, Roser Morante, María Teresa Martín-Valdivia, Luis Alfonso Ureña López

  8. Critical Survey of the Freely Available Arabic Corpora. arXiv 2017 paper bib

    Wajdi Zaghouani

  9. Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches. arXiv 2019 paper bib

    Shane Storks, Qiaozi Gao, Joyce Y. Chai

  10. Survey on Evaluation Methods for Dialogue Systems. Artif. Intell. Rev. 2021 paper bib

    Jan Deriu, Álvaro Rodrigo, Arantxa Otegi, Guillermo Echegoyen, Sophie Rosset, Eneko Agirre, Mark Cieliebak

  11. Survey on Publicly Available Sinhala Natural Language Processing Tools and Research. arXiv 2019 paper bib

    Nisansa de Silva

  12. The Great Misalignment Problem in Human Evaluation of NLP Methods. arXiv 2021 paper bib

    Mika Hämäläinen, Khalid Al-Najjar

  13. Towards Standard Criteria for human evaluation of Chatbots: A Survey. arXiv 2021 paper bib

    Hongru Liang, Huaqing Li

Semantics

  1. A reproducible survey on word embeddings and ontology-based methods for word similarity: Linear combinations outperform the state of the art. Eng. Appl. Artif. Intell. 2019 paper bib

    Juan J. Lastra-Díaz, Josu Goikoetxea, Mohamed Ali Hadj Taieb, Ana García-Serrano, Mohamed Ben Aouicha, Eneko Agirre

  2. A survey of loss functions for semantic segmentation. CIBCB 2020 paper bib

    Shruti Jadon

  3. A Survey of Paraphrasing and Textual Entailment Methods. J. Artif. Intell. Res. 2010 paper bib

    Ion Androutsopoulos, Prodromos Malakasiotis

  4. A Survey of Syntactic-Semantic Parsing Based on Constituent and Dependency Structures. arXiv 2020 paper bib

    Meishan Zhang

  5. A Survey on Semantic Parsing. AKBC 2019 paper bib

    Aishwarya Kamath, Rajarshi Das

  6. Argument Linking: A Survey and Forecast. arXiv 2021 paper bib

    William Gantt

  7. Corpus-Based Paraphrase Detection Experiments and Review. Inf. 2020 paper bib

    Tedo Vrbanec, Ana Mestrovic

  8. Diachronic word embeddings and semantic shifts: a survey. COLING 2018 paper bib

    Andrey Kutuzov, Lilja Øvrelid, Terrence Szymanski, Erik Velldal

  9. Distributional Measures of Semantic Distance: A Survey. arXiv 2012 paper bib

    Saif Mohammad, Graeme Hirst

  10. Evolution of Semantic Similarity - A Survey. ACM Comput. Surv. 2021 paper bib

    Dhivya Chandrasekaran, Vijay Mago

  11. Measuring Sentences Similarity: A Survey. Indian Journal of Science and Technology 2019 paper bib

    Mamdouh Farouk

  12. Semantic search on text and knowledge bases. Found. Trends Inf. Retr. 2016 paper bib

    Hannah Bast, Björn Buchhold, Elmar Haussmann

  13. Semantics, Modelling, and the Problem of Representation of Meaning - a Brief Survey of Recent Literature. arXiv 2014 paper bib

    Yarin Gal

  14. Survey of Computational Approaches to Lexical Semantic Change. arXiv 2018 paper bib

    Nina Tahmasebi, Lars Borin, Adam Jatowt

  15. The Knowledge Acquisition Bottleneck Problem in Multilingual Word Sense Disambiguation. IJCAI 2020 paper bib

    Tommaso Pasini

  16. Word Sense disambiguation: A Survey. ACM Comput. Surv. 2009 paper bib

    Roberto Navigli

  17. Word sense disambiguation: a survey. IJCTCM 2015 paper bib

    Alok Ranjan Pal, Diganta Saha

Sentiment Analysis, Stylistic Analysis and Argument Mining

  1. 360 degree view of cross-domain opinion classification: a survey. Artif. Intell. Rev. 2021 paper bib

    Rahul Kumar Singh, Manoj Kumar Sachan, R. B. Patel

  2. A Comprehensive Survey on Aspect Based Sentiment Analysis. arXiv 2020 paper bib

    Kaustubh Yadav

  3. A Survey of Sentiment Analysis in Social Media. Knowl. Inf. Syst. 2019 paper bib

    Lin Yue, Weitong Chen, Xue Li, Wanli Zuo, Minghao Yin

  4. A Survey on Sentiment and Emotion Analysis for Computational Literary Studies. ZFDG 2019 paper bib

    Evgeny Kim, Roman Klinger

  5. Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research. arXiv 2020 paper bib

    Soujanya Poria, Devamanyu Hazarika, Navonil Majumder, Rada Mihalcea

  6. Deep Learning for Aspect-Level Sentiment Classification: Survey, Vision, and Challenges. IEEE Access 2019 paper bib

    Jie Zhou, Jimmy Xiangji Huang, Qin Chen, Qinmin Vivian Hu, Tingting Wang, Liang He

  7. Deep Learning for Sentiment Analysis : A Survey. arXiv 2018 paper bib

    Lei Zhang, Shuai Wang, Bing Liu

  8. Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances. IEEE Access 2019 paper bib

    Soujanya Poria, Navonil Majumder, Rada Mihalcea, Eduard H. Hovy

  9. Fine-grained Financial Opinion Mining: A Survey and Research Agenda. arXiv 2020 paper bib

    Chung-Chi Chen, Hen-Hsen Huang, Hsin-Hsi Chen

  10. On Positivity Bias in Negative Reviews. ACL 2021 paper bib

    Madhusudhan Aithal, Chenhao Tan

  11. Sarcasm Detection: A Comparative Study. arXiv 2021 paper bib

    Hamed Yaghoobian, Hamid R. Arabnia, Khaled Rasheed

  12. Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal 2014 paper bib

    Walaa Medhat, Ahmed Hassan, Hoda Korashy

  13. Sentiment analysis for Arabic language: A brief survey of approaches and techniques. arXiv 2018 paper bib

    Mo'ath Alrefai, Hossam Faris, Ibrahim Aljarah

  14. Sentiment Analysis of Czech Texts: An Algorithmic Survey. ICAART 2019 paper bib

    Erion Çano, Ondrej Bojar

  15. Sentiment Analysis of Twitter Data: A Survey of Techniques. IJCAI 2016 paper bib

    Vishal.A.Kharde, Prof. Sheetal.Sonawane

  16. Sentiment Analysis on YouTube: A Brief Survey. arXiv 2015 paper bib

    Muhammad Zubair Asghar, Shakeel Ahmad, Afsana Marwat, Fazal Masood Kundi

  17. Sentiment/Subjectivity Analysis Survey for Languages other than English. Soc. Netw. Anal. Min. 2016 paper bib

    Mohammed Korayem, Khalifeh AlJadda, David J. Crandall

  18. Towards Argument Mining for Social Good: A Survey. ACL 2021 paper bib

    Eva Maria Vecchi, Neele Falk, Iman Jundi, Gabriella Lapesa

  19. Word Embeddings for Sentiment Analysis: A Comprehensive Empirical Survey. arXiv 2019 paper bib

    Erion Çano, Maurizio Morisio

Speech and Multimodality

  1. A Comprehensive Survey on Cross-modal Retrieval. arXiv 2016 paper bib

    Kaiye Wang, Qiyue Yin, Wei Wang, Shu Wu, Liang Wang

  2. A Multimodal Memes Classification: A Survey and Open Research Issues. arXiv 2020 paper bib

    Tariq Habib Afridi, Aftab Alam, Muhammad Numan Khan, Jawad Khan, Young-Koo Lee

  3. A Survey and Taxonomy of Adversarial Neural Networks for Text-to-Image Synthesis. WIREs Data Mining Knowl. Discov. 2020 paper bib

    Jorge Agnese, Jonathan Herrera, Haicheng Tao, Xingquan Zhu

  4. A Survey of Code-switched Speech and Language Processing. arXiv 2019 paper bib

    Sunayana Sitaram, Khyathi Raghavi Chandu, Sai Krishna Rallabandi, Alan W. Black

  5. A Survey of Deep Learning Approaches for OCR and Document Understanding. arXiv 2020 paper bib

    Nishant Subramani, Alexandre Matton, Malcolm Greaves, Adrian Lam

  6. A Survey of Recent DNN Architectures on the TIMIT Phone Recognition Task. TSD 2018 paper bib

    Josef Michálek, Jan Vanek

  7. A Survey of Voice Translation Methodologies - Acoustic Dialect Decoder. ICICES 2016 paper bib

    Hans Krupakar, Keerthika Rajvel, Bharathi B, Angel Deborah S, Vallidevi Krishnamurthy

  8. A Survey on Neural Speech Synthesis. arXiv 2021 paper bib

    Xu Tan, Tao Qin, Frank K. Soong, Tie-Yan Liu

  9. A Survey on Spoken Language Understanding: Recent Advances and New Frontiers. IJCAI 2021 paper bib

    Libo Qin, Tianbao Xie, Wanxiang Che, Ting Liu

  10. A Thorough Review on Recent Deep Learning Methodologies for Image Captioning. arXiv 2021 paper bib

    Ahmed Elhagry, Karima Kadaoui

  11. Accented Speech Recognition: A Survey. arXiv 2021 paper bib

    Arthur Hinsvark, Natalie Delworth, Miguel Del Rio, Quinten McNamara, Joshua Dong, Ryan Westerman, Michelle Huang, Joseph Palakapilly, Jennifer Drexler, Ilya Pirkin, Nishchal Bhandari, Miguel Jette

  12. Automatic Description Generation from Images: A Survey of Models, Datasets, and Evaluation Measures. J. Artif. Intell. Res. 2016 paper bib

    Raffaella Bernardi, Ruket Çakici, Desmond Elliott, Aykut Erdem, Erkut Erdem, Nazli Ikizler-Cinbis, Frank Keller, Adrian Muscat, Barbara Plank

  13. Automatic Speech Recognition using limited vocabulary: A survey. arXiv 2021 paper bib

    Jean Louis Ebongue Kedieng Fendji, Diane M. Tala, Blaise Omer Yenke, Marcellin Atemkeng

  14. Deep Emotion Recognition in Dynamic Data using Facial, Speech and Textual Cues: A Survey. TechRxiv 2021 paper bib

    Tao ZhangTao Zhang, Zhenhua Tan

  15. Image Captioning based on Deep Learning Methods: A Survey. arXiv 2019 paper bib

    Yiyu Wang, Jungang Xu, Yingfei Sun, Ben He

  16. Multimodal Intelligence: Representation Learning, Information Fusion, and Applications. IEEE J. Sel. Top. Signal Process. 2020 paper bib

    Chao Zhang, Zichao Yang, Xiaodong He, Li Deng

  17. Multimodal Machine Learning: A Survey and Taxonomy. IEEE Trans. Pattern Anal. Mach. Intell. 2019 paper bib

    Tadas Baltrusaitis, Chaitanya Ahuja, Louis-Philippe Morency

  18. Perspectives and Prospects on Transformer Architecture for Cross-Modal Tasks with Language and Vision. arXiv 2021 paper bib

    Andrew Shin, Masato Ishii, Takuya Narihira

  19. Recent Advances and Trends in Multimodal Deep Learning: A Review. arXiv 2021 paper bib

    Jabeen Summaira, Xi Li, Amin Muhammad Shoib, Songyuan Li, Jabbar Abdul

  20. Referring Expression Comprehension: A Survey of Methods and Datasets. IEEE Trans. Multim. 2021 paper bib

    Yanyuan Qiao, Chaorui Deng, Qi Wu

  21. Review of end-to-end speech synthesis technology based on deep learning. arXiv 2021 paper bib

    Zhaoxi Mu, Xinyu Yang, Yizhuo Dong

  22. Speech and Language Processing. Stanford 2019 paper bib

    Dan Jurafsky, James H. Martin

  23. Text Detection and Recognition in the Wild: A Review. arXiv 2020 paper bib

    Zobeir Raisi, Mohamed A. Naiel, Paul W. Fieguth, Steven Wardell, John S. Zelek

  24. Text Recognition in the Wild: A Survey. ACM Comput. Surv. 2021 paper bib

    Xiaoxue Chen, Lianwen Jin, Yuanzhi Zhu, Canjie Luo, Tianwei Wang

  25. Thank you for Attention: A survey on Attention-based Artificial Neural Networks for Automatic Speech Recognition. arXiv 2021 paper bib

    Priyabrata Karmakar, Shyh Wei Teng, Guojun Lu

  26. Unsupervised Automatic Speech Recognition: A Review. arXiv 2021 paper bib

    Hanan Aldarmaki, Asad Ullah, Nazar Zaki

Summarization

  1. A Survey of the State-of-the-Art Models in Neural Abstractive Text Summarization. IEEE Access 2021 paper bib

    Ayesha Ayub Syed, Ford Lumban Gaol, Tokuro Matsuo

  2. A Survey on Dialogue Summarization: Recent Advances and New Frontiers. arXiv 2021 paper bib

    Xiachong Feng, Xiaocheng Feng, Bing Qin

  3. A Survey on Neural Network-Based Summarization Methods. arXiv 2018 paper bib

    Yue Dong

  4. Abstractive Summarization: A Survey of the State of the Art. AAAI 2019 paper bib

    Hui Lin, Vincent Ng

  5. Automated text summarisation and evidence-based medicine: A survey of two domains. arXiv 2017 paper bib

    Abeed Sarker, Diego Mollá Aliod, Cécile Paris

  6. Automatic Keyword Extraction for Text Summarization: A Survey. arXiv 2017 paper bib

    Santosh Kumar Bharti, Korra Sathya Babu

  7. Automatic summarization of scientific articles: A survey. Journal of King Saud University - Computer and Information Sciences 2020 paper bib

    Nouf Ibrahim Altmami, Mohamed El Bachir Menai

  8. Deep Learning Based Abstractive Text Summarization: Approaches, Datasets, Evaluation Measures, and Challenges. Mathematical Problems in Engineering 2020 paper bib

    Dima Suleiman, Arafat Awajan

  9. From Standard Summarization to New Tasks and Beyond: Summarization with Manifold Information. IJCAI 2020 paper bib

    Shen Gao, Xiuying Chen, Zhaochun Ren, Dongyan Zhao, Rui Yan

  10. How to Evaluate a Summarizer: Study Design and Statistical Analysis for Manual Linguistic Quality Evaluation. EACL 2021 paper bib

    Julius Steen, Katja Markert

  11. Neural Abstractive Text Summarization with Sequence-to-Sequence Models. Trans. Data Sci. 2021 paper bib

    Tian Shi, Yaser Keneshloo, Naren Ramakrishnan, Chandan K. Reddy

  12. Recent automatic text summarization techniques: a survey. Artif. Intell. Rev. 2017 paper bib

    Mahak Gambhir, Vishal Gupta

  13. Text Summarization Techniques: A Brief Survey. arXiv 2017 paper bib

    Mehdi Allahyari, Seyed Amin Pouriyeh, Mehdi Assefi, Saeid Safaei, Elizabeth D. Trippe, Juan B. Gutierrez, Krys J. Kochut

  14. The Factual Inconsistency Problem in Abstractive Text Summarization: A Survey. arXiv 2021 paper bib

    Yi-Chong Huang, Xia-Chong Feng, Xiao-Cheng Feng, Bing Qin

  15. What Have We Achieved on Text Summarization?. EMNLP 2020 paper bib

    Dandan Huang, Leyang Cui, Sen Yang, Guangsheng Bao, Kun Wang, Jun Xie, Yue Zhang

  16. Multi-document Summarization via Deep Learning Techniques: A Survey. arXiv 2020 paper bib

    Congbo Ma, Wei Emma Zhang, Mingyu Guo, Hu Wang, Quan Z. Sheng

Tagging, Chunking, Syntax and Parsing

  1. A survey of cross-lingual features for zero-shot cross-lingual semantic parsing. arXiv 2019 paper bib

    Jingfeng Yang, Federico Fancellu, Bonnie L. Webber

  2. A Survey of Syntactic-Semantic Parsing Based on Constituent and Dependency Structures. arXiv 2020 paper bib

    Meishan Zhang

  3. A Survey on Recent Advances in Sequence Labeling from Deep Learning Models. arXiv 2020 paper bib

    Zhiyong He, Zanbo Wang, Wei Wei, Shanshan Feng, Xianling Mao, Sheng Jiang

  4. A Survey on Semantic Parsing. AKBC 2019 paper bib

    Aishwarya Kamath, Rajarshi Das

  5. A Survey on Semantic Parsing from the perspective of Compositionality. arXiv 2020 paper bib

    Pawan Kumar, Srikanta Bedathur

  6. Context Dependent Semantic Parsing: A Survey. COLING 2020 paper bib

    Zhuang Li, Lizhen Qu, Gholamreza Haffari

  7. Design Challenges and Misconceptions in Neural Sequence Labeling. COLING 2018 paper bib

    Jie Yang, Shuailong Liang, Yue Zhang

  8. Part‐of‐speech tagging. Wiley Interdisciplinary Reviews: Computational Statistics 2011 paper bib

    Angel R. Martinez

  9. Sememe knowledge computation: a review of recent advances in application and expansion of sememe knowledge bases. Frontiers Comput. Sci. 2021 paper bib

    Fanchao Qi, Ruobing Xie, Yuan Zang, Zhiyuan Liu, Maosong Sun

  10. Syntactic Parsing: A Survey. Computers and the Humanities 1989 paper bib

    Alton F. Sanders and Ruth H. Sanders

  11. Syntax Representation in Word Embeddings and Neural Networks - A Survey. ITAT 2020 paper bib

    Tomasz Limisiewicz, David Marecek

  12. The Gap of Semantic Parsing: A Survey on Automatic Math Word Problem Solvers. IEEE Trans. Pattern Anal. Mach. Intell. 2020 paper bib

    Dongxiang Zhang, Lei Wang, Luming Zhang, Bing Tian Dai, Heng Tao Shen

Text Classification

  1. A Survey of Active Learning for Text Classification using Deep Neural Networks. arXiv 2020 paper bib

    Christopher Schröder, Andreas Niekler

  2. A Survey of Naïve Bayes Machine Learning approach in Text Document Classification. IJCSIS 2010 paper bib

    K. A. Vidhya, G. Aghila

  3. A Survey on Data Augmentation for Text Classification. arXiv 2021 paper bib

    Markus Bayer, Marc-André Kaufhold, Christian Reuter

  4. A Survey on Natural Language Processing for Fake News Detection. LREC 2020 paper bib

    Ray Oshikawa, Jing Qian, William Yang Wang

  5. A survey on phrase structure learning methods for text classification. IJNLC 2014 paper bib

    Reshma Prasad, Mary Priya Sebastian

  6. A Survey on Stance Detection for Mis- and Disinformation Identification. arXiv 2021 paper bib

    Momchil Hardalov, Arnav Arora, Preslav Nakov, Isabelle Augenstein

  7. A Survey on Text Classification: From Shallow to Deep Learning. arXiv 2020 paper bib

    Qian Li, Hao Peng, Jianxin Li, Congying Xia, Renyu Yang, Lichao Sun, Philip S. Yu, Lifang He

  8. Automatic Language Identification in Texts: A Survey. J. Artif. Intell. Res. 2019 paper bib

    Tommi Jauhiainen, Marco Lui, Marcos Zampieri, Timothy Baldwin, Krister Lindén

  9. Deep Learning-based Text Classification: A Comprehensive Review. ACM Comput. Surv. 2021 paper bib

    Shervin Minaee, Nal Kalchbrenner, Erik Cambria, Narjes Nikzad, Meysam Chenaghlu, Jianfeng Gao

  10. Fake News Detection using Stance Classification: A Survey. arXiv 2019 paper bib

    Anders Edelbo Lillie, Emil Refsgaard Middelboe

  11. Semantic text classification: A survey of past and recent advances. Inf. Process. Manag. 2018 paper bib

    Berna Altinel, Murat Can Ganiz

  12. Text Classification Algorithms: A Survey. Inf. 2019 paper bib

    Kamran Kowsari, Kiana Jafari Meimandi, Mojtaba Heidarysafa, Sanjana Mendu, Laura E. Barnes, Donald E. Brown

Architectures

  1. A Practical Survey on Faster and Lighter Transformers. arXiv 2021 paper bib

    Quentin Fournier, Gaétan Marceau Caron, Daniel Aloise

  2. A Review of Binarized Neural Networks. Electronics 2019 paper bib

    Taylor Simons, Dah-Jye Lee

  3. A State-of-the-Art Survey on Deep Learning Theory and Architectures. Electronics 2019 paper bib

    Md Zahangir Alom, Tarek M. Taha, Chris Yakopcic, Stefan Westberg, Paheding Sidike, Mst Shamima Nasrin, Mahmudul Hasan, Brian C. Van Essen, Abdul A. S. Awwal and Vijayan K. Asari

  4. A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects. arXiv 2020 paper bib

    Zewen Li, Wenjie Yang, Shouheng Peng, Fan Liu

  5. A Survey of End-to-End Driving: Architectures and Training Methods. arXiv 2020 paper bib

    Ardi Tampuu, Maksym Semikin, Naveed Muhammad, Dmytro Fishman, Tambet Matiisen

  6. A Survey of Transformers. arXiv 2021 paper bib

    Tianyang Lin, Yuxin Wang, Xiangyang Liu, Xipeng Qiu

  7. A Survey on Activation Functions and their relation with Xavier and He Normal Initialization. arXiv 2020 paper bib

    Leonid Datta

  8. A Survey on Latent Tree Models and Applications. J. Artif. Intell. Res. 2013 paper bib

    Raphaël Mourad, Christine Sinoquet, Nevin Lianwen Zhang, Tengfei Liu, Philippe Leray

  9. A survey on modern trainable activation functions. Neural Networks 2021 paper bib

    Andrea Apicella, Francesco Donnarumma, Francesco Isgrò, Roberto Prevete

  10. A Survey on Vision Transformer. arXiv 2020 paper bib

    Kai Han, Yunhe Wang, Hanting Chen, Xinghao Chen, Jianyuan Guo, Zhenhua Liu, Yehui Tang, An Xiao, Chunjing Xu, Yixing Xu, Zhaohui Yang, Yiman Zhang, Dacheng Tao

  11. An Attentive Survey of Attention Models. ACM Trans. Intell. Syst. Technol. 2021 paper bib

    Sneha Chaudhari, Varun Mithal, Gungor Polatkan, Rohan Ramanath

  12. Attention mechanisms and deep learning for machine vision: A survey of the state of the art. arXiv 2021 paper bib

    Abdul Mueed Hafiz, Shabir Ahmad Parah, Rouf Ul Alam Bhat

  13. Big Networks: A Survey. Comput. Sci. Rev. 2020 paper bib

    Hayat Dino Bedru, Shuo Yu, Xinru Xiao, Da Zhang, Liangtian Wan, He Guo, Feng Xia

  14. Binary Neural Networks: A Survey. Pattern Recognit. 2020 paper bib

    Haotong Qin, Ruihao Gong, Xianglong Liu, Xiao Bai, Jingkuan Song, Nicu Sebe

  15. Deep Echo State Network (DeepESN): A Brief Survey. arXiv 2017 paper bib

    Claudio Gallicchio, Alessio Micheli

  16. Deep Tree Transductions - A Short Survey. INNSBDDL 2019 paper bib

    Davide Bacciu, Antonio Bruno

  17. Efficient Transformers: A Survey. arXiv 2020 paper bib

    Yi Tay, Mostafa Dehghani, Dara Bahri, Donald Metzler

  18. Pooling Methods in Deep Neural Networks, a Review. arXiv 2020 paper bib

    Hossein Gholamalinezhad, Hossein Khosravi

  19. Position Information in Transformers: An Overview. arXiv 2021 paper bib

    Philipp Dufter, Martin Schmitt, Hinrich Schütze

  20. Recent Advances in Convolutional Neural Networks. Pattern Recognit. 2018 paper bib

    Jiuxiang Gu, Zhenhua Wang, Jason Kuen, Lianyang Ma, Amir Shahroudy, Bing Shuai, Ting Liu, Xingxing Wang, Gang Wang, Jianfei Cai, Tsuhan Chen

  21. Sum-product networks: A survey. arXiv 2020 paper bib

    Iago París, Raquel Sánchez-Cauce, Francisco Javier Díez

  22. Survey of Dropout Methods for Deep Neural Networks. arXiv 2019 paper bib

    Alex Labach, Hojjat Salehinejad, Shahrokh Valaee

  23. Survey on the attention based RNN model and its applications in computer vision. arXiv 2016 paper bib

    Feng Wang, David M. J. Tax

  24. The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches. arXiv 2018 paper bib

    Md. Zahangir Alom, Tarek M. Taha, Christopher Yakopcic, Stefan Westberg, Paheding Sidike, Mst Shamima Nasrin, Brian C. Van Essen, Abdul A. S. Awwal, Vijayan K. Asari

  25. The NLP Cookbook: Modern Recipes for Transformer based Deep Learning Architectures. IEEE Access 2021 paper bib

    Sushant Singh, Ausif Mahmood

  26. Transformers in Vision: A Survey. arXiv 2021 paper bib

    Salman H. Khan, Muzammal Naseer, Munawar Hayat, Syed Waqas Zamir, Fahad Shahbaz Khan, Mubarak Shah

  27. Understanding LSTM - a tutorial into Long Short-Term Memory Recurrent Neural Networks. arXiv 2019 paper bib

    Ralf C. Staudemeyer, Eric Rothstein Morris

AutoML

  1. A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions. ACM Comput. Surv. 2021 paper bib

    Pengzhen Ren, Yun Xiao, Xiaojun Chang, Poyao Huang, Zhihui Li, Xiaojiang Chen, Xin Wang

  2. A Comprehensive Survey on Hardware-Aware Neural Architecture Search. arXiv 2021 paper bib

    Hadjer Benmeziane, Kaoutar El Maghraoui, Hamza Ouarnoughi, Smaïl Niar, Martin Wistuba, Naigang Wang

  3. A Review of Meta-Reinforcement Learning for Deep Neural Networks Architecture Search. arXiv 2018 paper bib

    Yesmina Jaâfra, Jean Luc Laurent, Aline Deruyver, Mohamed Saber Naceur

  4. A Survey on Neural Architecture Search. arXiv 2019 paper bib

    Martin Wistuba, Ambrish Rawat, Tejaswini Pedapati

  5. Automated Machine Learning on Graphs: A Survey. IJCAI 2021 paper bib

    Ziwei Zhang, Xin Wang, Wenwu Zhu

  6. AutoML: A Survey of the State-of-the-Art. Knowl. Based Syst. 2021 paper bib

    Xin He, Kaiyong Zhao, Xiaowen Chu

  7. Benchmark and Survey of Automated Machine Learning Frameworks. J. Artif. Intell. Res. 2021 paper bib

    Marc-André Zöller, Marco F. Huber

  8. Neural Architecture Search: A Survey. J. Mach. Learn. Res. 2019 paper bib

    Thomas Elsken, Jan Hendrik Metzen, Frank Hutter

  9. Reinforcement learning for neural architecture search: A review. Image Vis. Comput. 2019 paper bib

    Yesmina Jaâfra, Jean Luc Laurent, Aline Deruyver, Mohamed Saber Naceur

Bayesian Methods

  1. A survey of non-exchangeable priors for Bayesian nonparametric models. IEEE Trans. Pattern Anal. Mach. Intell. 2015 paper bib

    Nicholas J. Foti, Sinead A. Williamson

  2. A Survey on Bayesian Deep Learning. ACM Comput. Surv. 2020 paper bib

    Hao Wang, Dit-Yan Yeung

  3. Bayesian Neural Networks: An Introduction and Survey. arXiv 2020 paper bib

    Ethan Goan, Clinton Fookes

  4. Bayesian Nonparametric Space Partitions: A Survey. IJCAI 2021 paper bib

    Xuhui Fan, Bin Li, Ling Luo, Scott A. Sisson

  5. Deep Bayesian Active Learning, A Brief Survey on Recent Advances. arXiv 2020 paper bib

    Salman Mohamadi, Hamidreza Amindavar

  6. Hands-on Bayesian Neural Networks - a Tutorial for Deep Learning Users. arXiv 2020 paper bib

    Laurent Valentin Jospin, Wray L. Buntine, Farid Boussaïd, Hamid Laga, Mohammed Bennamoun

  7. Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proc. IEEE 2016 paper bib

    Bobak Shahriari, Kevin Swersky, Ziyu Wang, Ryan P. Adams, Nando de Freitas

Classification, Clustering and Regression

  1. A continual learning survey: Defying forgetting in classification tasks. TPAMI 2021 paper bib

    Matthias De Lange, Rahaf Aljundi, Marc Masana, Sarah Parisot, Xu Jia, Ales Leonardis, Gregory Slabaugh, Tinne Tuytelaars

  2. A Survey of Classification Techniques in the Area of Big Data. arXiv 2015 paper bib

    Praful Koturwar, Sheetal Girase, Debajyoti Mukhopadhyay

  3. A Survey of Constrained Gaussian Process Regression: Approaches and Implementation Challenges. arXiv 2020 paper bib

    Laura P. Swiler, Mamikon Gulian, Ari Frankel, Cosmin Safta, John D. Jakeman

  4. A Survey of Machine Learning Methods and Challenges for Windows Malware Classification. arXiv 2020 paper bib

    Edward Raff, Charles Nicholas

  5. A Survey of Methods for Managing the Classification and Solution of Data Imbalance Problem. arXiv 2020 paper bib

    Khan Md. Hasib, Md. Sadiq Iqbal, Faisal Muhammad Shah, Jubayer Al Mahmud, Mahmudul Hasan Popel, Md. Imran Hossain Showrov, Shakil Ahmed, Obaidur Rahman

  6. A Survey of Techniques All Classifiers Can Learn from Deep Networks: Models, Optimizations, and Regularization. arXiv 2019 paper bib

    Alireza Ghods, Diane J. Cook

  7. A Survey on Multi-View Clustering. arXiv 2017 paper bib

    Guoqing Chao, Shiliang Sun, Jinbo Bi

  8. Comprehensive Comparative Study of Multi-Label Classification Methods. arXiv 2021 paper bib

    Jasmin Bogatinovski, Ljupco Todorovski, Saso Dzeroski, Dragi Kocev

  9. Deep learning for time series classification: a review. Data Min. Knowl. Discov. 2019 paper bib

    Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber, Lhassane Idoumghar, Pierre-Alain Muller

  10. How Complex is your classification problem?: A survey on measuring classification complexity. ACM Comput. Surv. 2019 paper bib

    Ana Carolina Lorena, Luís Paulo F. Garcia, Jens Lehmann, Marcílio Carlos Pereira de Souto, Tin Kam Ho

Computer Vision

  1. 3D Object Detection for Autonomous Driving: A Survey. arXiv 2021 paper bib

    Rui Qian, Xin Lai, Xirong Li

  2. A Survey of Black-Box Adversarial Attacks on Computer Vision Models. arXiv 2019 paper bib

    Siddhant Bhambri, Sumanyu Muku, Avinash Tulasi, Arun Balaji Buduru

  3. A survey of loss functions for semantic segmentation. CIBCB 2020 paper bib

    Shruti Jadon

  4. A Survey of Modern Deep Learning based Object Detection Models. arXiv 2021 paper bib

    Syed Sahil Abbas Zaidi, Mohammad Samar Ansari, Asra Aslam, Nadia Kanwal, Mamoona Naveed Asghar, Brian Lee

  5. A survey on applications of augmented, mixed and virtual reality for nature and environment. HCI 2021 paper bib

    Jason R. Rambach, Gergana Lilligreen, Alexander Schäfer, Ramya Bankanal, Alexander Wiebel, Didier Stricker

  6. A survey on deep hashing for image retrieval. arXiv 2020 paper bib

    Xiaopeng Zhang

  7. A Survey on Deep Learning in Medical Image Analysis. Medical Image Anal. 2017 paper bib

    Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen A. W. M. van der Laak, Bram van Ginneken, Clara I. Sánchez

  8. A Survey on Deep Learning Technique for Video Segmentation. arXiv 2021 paper bib

    Wenguan Wang, Tianfei Zhou, Fatih Porikli, David J. Crandall, Luc Van Gool

  9. A Technical Survey and Evaluation of Traditional Point Cloud Clustering Methods for LiDAR Panoptic Segmentation. ICCVW 2021 paper bib

    Yiming Zhao, Xiao Zhang, Xinming Huang

  10. Advances in adversarial attacks and defenses in computer vision: A survey. IEEE Access 2021 paper bib

    Naveed Akhtar, Ajmal Mian, Navid Kardan, Mubarak Shah

  11. Adversarial Examples on Object Recognition: A Comprehensive Survey. ACM Comput. Surv. 2020 paper bib

    Alexandru Constantin Serban, Erik Poll, Joost Visser

  12. Adversarial Machine Learning in Image Classification: A Survey Towards the Defender's Perspective. arXiv 2020 paper bib

    Gabriel Resende Machado, Eugênio Silva, Ronaldo Ribeiro Goldschmidt

  13. Affective Image Content Analysis: Two Decades Review and New Perspectives. arXiv 2021 paper bib

    Sicheng Zhao, Xingxu Yao, Jufeng Yang, Guoli Jia, Guiguang Ding, Tat-Seng Chua, Björn W. Schuller, Kurt Keutzer

  14. Applications of Artificial Neural Networks in Microorganism Image Analysis: A Comprehensive Review from Conventional Multilayer Perceptron to Popular Convolutional Neural Network and Potential Visual Transformer. arXiv 2021 paper bib

    Jinghua Zhang, Chen Li, Marcin Grzegorzek

  15. Automatic Gaze Analysis: A Survey of Deep Learning based Approaches. arXiv 2021 paper bib

    Shreya Ghosh, Abhinav Dhall, Munawar Hayat, Jarrod Knibbe, Qiang Ji

  16. Bridging Gap between Image Pixels and Semantics via Supervision: A Survey. arXiv 2021 paper bib

    Jiali Duan, C.-C. Jay Kuo

  17. Deep Learning for 3D Point Cloud Understanding: A Survey. arXiv 2020 paper bib

    Haoming Lu, Humphrey Shi

  18. Deep Learning for Embodied Vision Navigation: A Survey. arXiv 2021 paper bib

    Fengda Zhu, Yi Zhu, Xiaodan Liang, Xiaojun Chang

  19. Deep Learning for Image Super-resolution: A Survey. IEEE Trans. Pattern Anal. Mach. Intell. 2021 paper bib

    Zhihao Wang, Jian Chen, Steven C. H. Hoi

  20. Deep Learning for Instance Retrieval: A Survey. arXiv 2021 paper bib

    Wei Chen, Yu Liu, Weiping Wang, Erwin Bakker, Theodoros Georgiou, Paul Fieguth, Li Liu, Michael S. Lew

  21. Deep Learning for Scene Classification: A Survey. arXiv 2021 paper bib

    Delu Zeng, Minyu Liao, Mohammad Tavakolian, Yulan Guo, Bolei Zhou, Dewen Hu, Matti Pietikäinen, Li Liu

  22. Image/Video Deep Anomaly Detection: A Survey. arXiv 2021 paper bib

    Bahram Mohammadi, Mahmood Fathy, Mohammad Sabokrou

  23. Image-to-Image Translation: Methods and Applications. arXiv 2021 paper bib

    Yingxue Pang, Jianxin Lin, Tao Qin, Zhibo Chen

  24. Imbalance Problems in Object Detection: A Review. IEEE Trans. Pattern Anal. Mach. Intell. 2021 paper bib

    Kemal Oksuz, Baris Can Cam, Sinan Kalkan, Emre Akbas

  25. MmWave Radar and Vision Fusion for Object Detection in Autonomous Driving: A Review. arXiv 2021 paper bib

    Zhiqing Wei, Fengkai Zhang, Shuo Chang, Yangyang Liu, Huici Wu, Zhiyong Feng

  26. Object Detection in 20 Years: A Survey. arXiv 2019 paper bib

    Zhengxia Zou, Zhenwei Shi, Yuhong Guo, Jieping Ye

  27. The Impact of Machine Learning on 2D/3D Registration for Image-guided Interventions: A Systematic Review and Perspective. Frontiers Robotics AI 2021 paper bib

    Mathias Unberath, Cong Gao, Yicheng Hu, Max Judish, Russell H. Taylor, Mehran Armand, Robert B. Grupp

  28. The Need and Status of Sea Turtle Conservation and Survey of Associated Computer Vision Advances. arXiv 2021 paper bib

    Aditya Jyoti Paul

Contrastive Learning

  1. A Survey on Contrastive Self-supervised Learning. arXiv 2020 paper bib

    Ashish Jaiswal, Ashwin Ramesh Babu, Mohammad Zaki Zadeh, Debapriya Banerjee, Fillia Makedon

  2. Contrastive Representation Learning: A Framework and Review. IEEE Access 2020 paper bib

    Phuc H. Le-Khac, Graham Healy, Alan F. Smeaton

  3. Self-supervised Learning: Generative or Contrastive. arXiv 2020 paper bib

    Xiao Liu, Fanjin Zhang, Zhenyu Hou, Zhaoyu Wang, Li Mian, Jing Zhang, Jie Tang

Curriculum Learning

  1. A Survey on Curriculum Learning. TPAMI 2021 paper bib

    Xin Wang, Yudong Chen, Wenwu Zhu

  2. Automatic Curriculum Learning For Deep RL: A Short Survey. IJCAI 2020 paper bib

    Rémy Portelas, Cédric Colas, Lilian Weng, Katja Hofmann, Pierre-Yves Oudeyer

  3. Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey. J. Mach. Learn. Res. 2020 paper bib

    Sanmit Narvekar, Bei Peng, Matteo Leonetti, Jivko Sinapov, Matthew E. Taylor, Peter Stone

  4. Curriculum Learning: A Survey. arXiv 2021 paper bib

    Petru Soviany, Radu Tudor Ionescu, Paolo Rota, Nicu Sebe

Data Augmentation

  1. A survey on Image Data Augmentation for Deep Learning. J. Big Data 2019 paper bib

    Connor Shorten, Taghi M. Khoshgoftaar

  2. An Empirical Survey of Data Augmentation for Time Series Classification with Neural Networks. arXiv 2020 paper bib

    Brian Kenji Iwana, Seiichi Uchida

  3. Time Series Data Augmentation for Deep Learning: A Survey. IJCAI 2021 paper bib

    Qingsong Wen, Liang Sun, Fan Yang, Xiaomin Song, Jingkun Gao, Xue Wang, Huan Xu

Deep Learning General Methods

  1. A Survey of Deep Active Learning. ACM Comput. Surv. 2022 paper bib

    Pengzhen Ren, Yun Xiao, Xiaojun Chang, Po-Yao Huang, Zhihui Li, Brij B. Gupta, Xiaojiang Chen, Xin Wang

  2. A Survey of Deep Learning for Data Caching in Edge Network. Informatics 2020 paper bib

    Yantong Wang, Vasilis Friderikos

  3. A Survey of Deep Learning for Scientific Discovery. arXiv 2020 paper bib

    Maithra Raghu, Eric Schmidt

  4. A Survey of Label-noise Representation Learning: Past, Present and Future. arXiv 2020 paper bib

    Bo Han, Quanming Yao, Tongliang Liu, Gang Niu, Ivor W. Tsang, James T. Kwok, Masashi Sugiyama

  5. A Survey of Neuromorphic Computing and Neural Networks in Hardware. arXiv 2017 paper bib

    Catherine D. Schuman, Thomas E. Potok, Robert M. Patton, J. Douglas Birdwell, Mark E. Dean, Garrett S. Rose, James S. Plank

  6. A Survey of Uncertainty in Deep Neural Networks. arXiv 2021 paper bib

    Jakob Gawlikowski, Cedrique Rovile Njieutcheu Tassi, Mohsin Ali, Jongseok Lee, Matthias Humt, Jianxiang Feng, Anna M. Kruspe, Rudolph Triebel, Peter Jung, Ribana Roscher, Muhammad Shahzad, Wen Yang, Richard Bamler, Xiao Xiang Zhu

  7. A Survey on Active Deep Learning: From Model-driven to Data-driven. arXiv 2021 paper bib

    Peng Liu, Lizhe Wang, Guojin He, Lei Zhao

  8. A Survey on Assessing the Generalization Envelope of Deep Neural Networks: Predictive Uncertainty, Out-of-distribution and Adversarial Samples. arXiv 2020 paper bib

    Julia Lust, Alexandru Paul Condurache

  9. A Survey on Concept Factorization: From Shallow to Deep Representation Learning. Inf. Process. Manag. 2021 paper bib

    Zhao Zhang, Yan Zhang, Mingliang Xu, Li Zhang, Yi Yang, Shuicheng Yan

  10. A Survey on Deep Hashing Methods. arXiv 2020 paper bib

    Xiao Luo, Chong Chen, Huasong Zhong, Hao Zhang, Minghua Deng, Jianqiang Huang, Xiansheng Hua

  11. A Survey on Deep Learning with Noisy Labels: How to train your model when you cannot trust on the annotations?. SIBGRAPI 2020 paper bib

    Filipe R. Cordeiro, Gustavo Carneiro

  12. A Survey on Dynamic Network Embedding. arXiv 2020 paper bib

    Yu Xie, Chunyi Li, Bin Yu, Chen Zhang, Zhouhua Tang

  13. A Survey on Network Embedding. IEEE Trans. Knowl. Data Eng. 2019 paper bib

    Peng Cui, Xiao Wang, Jian Pei, Wenwu Zhu

  14. A Tutorial on Network Embeddings. arXiv 2018 paper bib

    Haochen Chen, Bryan Perozzi, Rami Al-Rfou, Steven Skiena

  15. Continual Lifelong Learning with Neural Networks: A Review. Neural Networks 2019 paper bib

    German Ignacio Parisi, Ronald Kemker, Jose L. Part, Christopher Kanan, Stefan Wermter

  16. Convergence of Edge Computing and Deep Learning: A Comprehensive Survey. IEEE Commun. Surv. Tutorials 2020 paper bib

    Xiaofei Wang, Yiwen Han, Victor C. M. Leung, Dusit Niyato, Xueqiang Yan, Xu Chen

  17. Deep learning. Nat. 2015 paper bib

    Yann LeCun, Yoshua Bengio, Geoffrey Hinton

  18. Deep Learning for Matching in Search and Recommendation. SIGIR 2018 paper bib

    Jun Xu, Xiangnan He, Hang Li

  19. Deep Learning Theory Review: An Optimal Control and Dynamical Systems Perspective. arXiv 2019 paper bib

    Guan-Horng Liu, Evangelos A. Theodorou

  20. Dynamic Neural Networks: A Survey. arXiv 2021 paper bib

    Yizeng Han, Gao Huang, Shiji Song, Le Yang, Honghui Wang, Yulin Wang

  21. Embracing Change: Continual Learning in Deep Neural Networks. Trends in Cognitive Sciences 2020 paper bib

    Raia Hadsell, Dushyant Rao, Andrei A. Rusu, Razvan Pascanu

  22. Geometric deep learning: going beyond Euclidean data. IEEE Signal Process. Mag. 2017 paper bib

    Michael M. Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, Pierre Vandergheynst

  23. Heuristic design of fuzzy inference systems: A review of three decades of research. Eng. Appl. Artif. Intell. 2019 paper bib

    Varun Ojha, Ajith Abraham, Václav Snásel

  24. Imitation Learning: Progress, Taxonomies and Opportunities. arXiv 2021 paper bib

    Boyuan Zheng, Sunny Verma, Jianlong Zhou, Ivor W. Tsang, Fang Chen

  25. Improving Deep Learning Models via Constraint-Based Domain Knowledge: a Brief Survey. arXiv 2020 paper bib

    Andrea Borghesi, Federico Baldo, Michela Milano

  26. Learning from Noisy Labels with Deep Neural Networks: A Survey. arXiv 2020 paper bib

    Hwanjun Song, Minseok Kim, Dongmin Park, Jae-Gil Lee

  27. Model Complexity of Deep Learning: A Survey. Knowl. Inf. Syst. 2021 paper bib

    Xia Hu, Lingyang Chu, Jian Pei, Weiqing Liu, Jiang Bian

  28. Network representation learning: A macro and micro view. AI Open 2021 paper bib

    Xueyi Liu, Jie Tang

  29. Network Representation Learning: A Survey. IEEE Trans. Big Data 2020 paper bib

    Daokun Zhang, Jie Yin, Xingquan Zhu, Chengqi Zhang

  30. Network representation learning: an overview. SCIENTIA SINICA Informationis 2017 paper bib

    Cunchao TU, Cheng YANG, Zhiyuan LIU, Maosong SUN

  31. Opportunities and Challenges in Deep Learning Adversarial Robustness: A Survey. arXiv 2020 paper bib

    Samuel Henrique Silva, Peyman Najafirad

  32. Recent advances in deep learning theory. arXiv 2020 paper bib

    Fengxiang He, Dacheng Tao

  33. Relational inductive biases, deep learning, and graph networks. arXiv 2018 paper bib

    Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinícius Flores Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, Çaglar Gülçehre, H. Francis Song, Andrew J. Ballard, Justin Gilmer, George E. Dahl, Ashish Vaswani, Kelsey R. Allen, Charles Nash, Victoria Langston, Chris Dyer, Nicolas Heess, Daan Wierstra, Pushmeet Kohli, Matthew Botvinick, Oriol Vinyals, Yujia Li, Razvan Pascanu

  34. Representation Learning: A Review and New Perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 2013 paper bib

    Yoshua Bengio, Aaron C. Courville, Pascal Vincent

  35. Review: Ordinary Differential Equations For Deep Learning. arXiv 2019 paper bib

    Xinshi Chen

  36. Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks. J. Mach. Learn. Res. 2021 paper bib

    Torsten Hoefler, Dan Alistarh, Tal Ben-Nun, Nikoli Dryden, Alexandra Peste

  37. Survey of Expressivity in Deep Neural Networks. NIPS 2016 paper bib

    Maithra Raghu, Ben Poole, Jon Kleinberg, Surya Ganguli, Jascha Sohl-Dickstein

  38. Survey of reasoning using Neural networks. arXiv 2017 paper bib

    Amit Sahu

  39. The Deep Learning Compiler: A Comprehensive Survey. IEEE Trans. Parallel Distributed Syst. 2021 paper bib

    Mingzhen Li, Yi Liu, Xiaoyan Liu, Qingxiao Sun, Xin You, Hailong Yang, Zhongzhi Luan, Lin Gan, Guangwen Yang, Depei Qian

  40. The Modern Mathematics of Deep Learning. arXiv 2021 paper bib

    Julius Berner, Philipp Grohs, Gitta Kutyniok, Philipp Petersen

  41. Time Series Data Imputation: A Survey on Deep Learning Approaches. arXiv 2020 paper bib

    Chenguang Fang, Chen Wang

  42. Time-series forecasting with deep learning: a survey. Philosophical Transactions of the Royal Society A 2021 paper bib

    Bryan Lim, Stefan Zohren

  43. Tutorial on Variational Autoencoders. arXiv 2016 paper bib

    Carl Doersch

  44. 网络表示学习算法综述. 计算机科学 2020 paper bib

    丁钰, 魏浩, 潘志松, 刘鑫

Deep Reinforcement Learning

  1. A Short Survey On Memory Based Reinforcement Learning. arXiv 2019 paper bib

    Dhruv Ramani

  2. A Short Survey on Probabilistic Reinforcement Learning. arXiv 2019 paper bib

    Reazul Hasan Russel

  3. A survey of benchmarking frameworks for reinforcement learning. arXiv 2020 paper bib

    Belinda Stapelberg, Katherine M. Malan

  4. A Survey of Exploration Strategies in Reinforcement Learning. McGill University 2003 paper bib

    R. McFarlane

  5. A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress. Artif. Intell. 2021 paper bib

    Saurabh Arora, Prashant Doshi

  6. A Survey of Reinforcement Learning Algorithms for Dynamically Varying Environments. ACM Comput. Surv. 2021 paper bib

    Sindhu Padakandla

  7. A Survey of Reinforcement Learning Informed by Natural Language. IJCAI 2019 paper bib

    Jelena Luketina, Nantas Nardelli, Gregory Farquhar, Jakob N. Foerster, Jacob Andreas, Edward Grefenstette, Shimon Whiteson, Tim Rocktäschel

  8. A Survey of Reinforcement Learning Techniques: Strategies, Recent Development, and Future Directions. arXiv 2020 paper bib

    Amit Kumar Mondal

  9. A Survey on Deep Reinforcement Learning for Audio-Based Applications. arXiv 2021 paper bib

    Siddique Latif, Heriberto Cuayáhuitl, Farrukh Pervez, Fahad Shamshad, Hafiz Shehbaz Ali, Erik Cambria

  10. A Survey on Deep Reinforcement Learning for Data Processing and Analytics. arXiv 2021 paper bib

    Qingpeng Cai, Can Cui, Yiyuan Xiong, Wei Wang, Zhongle Xie, Meihui Zhang

  11. A survey on intrinsic motivation in reinforcement learning. arXiv 2019 paper bib

    Arthur Aubret, Laëtitia Matignon, Salima Hassas

  12. A Survey on Reinforcement Learning for Combinatorial Optimization. arXiv 2020 paper bib

    Yunhao Yang, Andrew B. Whinston

  13. A Survey on Reproducibility by Evaluating Deep Reinforcement Learning Algorithms on Real-World Robots. CoRL 2019 paper bib

    Nicolai A. Lynnerup, Laura Nolling, Rasmus Hasle, John Hallam

  14. Adapting Behaviour via Intrinsic Reward: A Survey and Empirical Study. arXiv 2019 paper bib

    Cam Linke, Nadia M. Ady, Martha White, Thomas Degris, Adam White

  15. Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics. Mathematics 2020 paper bib

    Amirhosein Mosavi, Yaser Faghan, Pedram Ghamisi, Puhong Duan, Sina Faizollahzadeh Ardabili, Ely Salwana, Shahab S. Band

  16. Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey. J. Mach. Learn. Res. 2020 paper bib

    Sanmit Narvekar, Bei Peng, Matteo Leonetti, Jivko Sinapov, Matthew E. Taylor, Peter Stone

  17. Deep Model-Based Reinforcement Learning for High-Dimensional Problems, a Survey. arXiv 2020 paper bib

    Aske Plaat, Walter Kosters, Mike Preuss

  18. Deep Reinforcement Learning for Clinical Decision Support: A Brief Survey. arXiv 2019 paper bib

    Siqi Liu, Kee Yuan Ngiam, Mengling Feng

  19. Deep Reinforcement Learning for Demand Driven Services in Logistics and Transportation Systems: A Survey. arXiv 2021 paper bib

    Zefang Zong, Tao Feng, Tong Xia, Depeng Jin, Yong Li

  20. Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey. IEEE Trans. Intell. Transp. Syst. 2022 paper bib

    Ammar Haydari, Yasin Yilmaz

  21. Deep Reinforcement Learning in Quantitative Algorithmic Trading: A Review. arXiv 2021 paper bib

    Tidor-Vlad Pricope

  22. Deep Reinforcement Learning: A Brief Survey. IEEE Signal Process. Mag. 2017 paper bib

    Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, Anil Anthony Bharath

  23. Deep Reinforcement Learning: An Overview. arXiv 2017 paper bib

    Yuxi Li

  24. Derivative-Free Reinforcement Learning: A Review. Frontiers Comput. Sci. 2021 paper bib

    Hong Qian, Yang Yu

  25. Explainable Reinforcement Learning for Broad-XAI: A Conceptual Framework and Survey. arXiv 2021 paper bib

    Richard Dazeley, Peter Vamplew, Francisco Cruz

  26. Feature-Based Aggregation and Deep Reinforcement Learning: A Survey and Some New Implementations. IEEE CAA J. Autom. Sinica 2019 paper bib

    Dimitri P. Bertsekas

  27. Model-based Reinforcement Learning: A Survey. arXiv 2020 paper bib

    Thomas M. Moerland, Joost Broekens, Catholijn M. Jonker

  28. Reinforcement Learning for Combinatorial Optimization: A Survey. Comput. Oper. Res. 2021 paper bib

    Nina Mazyavkina, Sergey Sviridov, Sergei Ivanov, Evgeny Burnaev

  29. Reinforcement Learning in Healthcare: A Survey. arXiv 2019 paper bib

    Chao Yu, Jiming Liu, Shamim Nemati

  30. Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey. SSCI 2020 paper bib

    Wenshuai Zhao, Jorge Peña Queralta, Tomi Westerlund

  31. Survey on reinforcement learning for language processing. arXiv 2021 paper bib

    Víctor Uc-Cetina, Nicolás Navarro-Guerrero, Anabel Martín-González, Cornelius Weber, Stefan Wermter

  32. Tutorial and Survey on Probabilistic Graphical Model and Variational Inference in Deep Reinforcement Learning. SSCI 2019 paper bib

    Xudong Sun, Bernd Bischl

Federated Learning

  1. A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection. arXiv 2019 paper bib

    Qinbin Li, Zeyi Wen, Zhaomin Wu, Sixu Hu, Naibo Wang, Xu Liu, Bingsheng He

  2. Achieving Security and Privacy in Federated Learning Systems: Survey, Research Challenges and Future Directions. Eng. Appl. Artif. Intell. 2021 paper bib

    Alberto Blanco-Justicia, Josep Domingo-Ferrer, Sergio Martínez, David Sánchez, Adrian Flanagan, Kuan Eeik Tan

  3. Advances and Open Problems in Federated Learning. Found. Trends Mach. Learn. 2021 paper bib

    Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista A. Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Hubert Eichner, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaïd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Hang Qi, Daniel Ramage, Ramesh Raskar, Mariana Raykova, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao

  4. Fusion of Federated Learning and Industrial Internet of Things: A Survey. arXiv 2021 paper bib

    Parimala M., R. M. Swarna Priya, Quoc-Viet Pham, Kapal Dev, Praveen Kumar Reddy Maddikunta, Thippa Reddy Gadekallu, Thien Huynh-The

  5. Privacy and Robustness in Federated Learning: Attacks and Defenses. arXiv 2020 paper bib

    Lingjuan Lyu, Han Yu, Xingjun Ma, Lichao Sun, Jun Zhao, Qiang Yang, Philip S. Yu

  6. Threats to Federated Learning: A Survey. arXiv 2020 paper bib

    Lingjuan Lyu, Han Yu, Qiang Yang

  7. Towards Utilizing Unlabeled Data in Federated Learning: A Survey and Prospective. arXiv 2020 paper bib

    Yilun Jin, Xiguang Wei, Yang Liu, Qiang Yang

Few-Shot and Zero-Shot Learning

  1. A Survey of Zero-Shot Learning: Settings, Methods, and Applications. ACM Trans. Intell. Syst. Technol. 2019 paper bib

    Wei Wang, Vincent W. Zheng, Han Yu, Chunyan Miao

  2. Generalizing from a Few Examples: A Survey on Few-Shot Learning. ACM Comput. Surv. 2020 paper bib

    Yaqing Wang, Quanming Yao, James T. Kwok, Lionel M. Ni

  3. Learning from Few Samples: A Survey. arXiv 2020 paper bib

    Nihar Bendre, Hugo Terashima-Marín, Peyman Najafirad

  4. Learning from Very Few Samples: A Survey. arXiv 2020 paper bib

    Jiang Lu, Pinghua Gong, Jieping Ye, Changshui Zhang

General Machine Learning

  1. A Comprehensive Survey on Outlying Aspect Mining Methods. arXiv 2020 paper bib

    Durgesh Samariya, Jiangang Ma, Sunil Aryal

  2. A Survey of Adaptive Resonance Theory Neural Network Models for Engineering Applications. Neural Networks 2019 paper bib

    Leonardo Enzo Brito da Silva, Islam Elnabarawy, Donald C. Wunsch II

  3. A survey of dimensionality reduction techniques. arXiv 2014 paper bib

    Carlos Oscar Sánchez Sorzano, Javier Vargas, Alberto Domingo Pascual-Montano

  4. A Survey of Human-in-the-loop for Machine Learning. arXiv 2021 paper bib

    Xingjiao Wu, Luwei Xiao, Yixuan Sun, Junhang Zhang, Tianlong Ma, Liang He

  5. A Survey of Learning Causality with Data: Problems and Methods. ACM Comput. Surv. 2020 paper bib

    Ruocheng Guo, Lu Cheng, Jundong Li, P. Richard Hahn, Huan Liu

  6. A Survey of Predictive Modelling under Imbalanced Distributions. arXiv 2015 paper bib

    Paula Branco, Luís Torgo, Rita P. Ribeiro

  7. A Survey On (Stochastic Fractal Search) Algorithm. arXiv 2021 paper bib

    Mohammed ElKomy

  8. A Survey on Data Collection for Machine Learning: a Big Data - AI Integration Perspective. IEEE Trans. Knowl. Data Eng. 2021 paper bib

    Yuji Roh, Geon Heo, Steven Euijong Whang

  9. A Survey on Distributed Machine Learning. ACM Comput. Surv. 2020 paper bib

    Joost Verbraeken, Matthijs Wolting, Jonathan Katzy, Jeroen Kloppenburg, Tim Verbelen, Jan S. Rellermeyer

  10. A survey on feature weighting based K-Means algorithms. J. Classif. 2016 paper bib

    Renato Cordeiro de Amorim

  11. A survey on graph kernels. Appl. Netw. Sci. 2020 paper bib

    Nils M. Kriege, Fredrik D. Johansson, Christopher Morris

  12. A Survey on Large-scale Machine Learning. arXiv 2020 paper bib

    Meng Wang, Weijie Fu, Xiangnan He, Shijie Hao, Xindong Wu

  13. A Survey on Optimal Transport for Machine Learning: Theory and Applications. arXiv 2021 paper bib

    Luis Caicedo Torres, Luiz Manella Pereira, M. Hadi Amini

  14. A Survey on Resilient Machine Learning. arXiv 2017 paper bib

    Atul Kumar, Sameep Mehta

  15. A Survey on Surrogate Approaches to Non-negative Matrix Factorization. Vietnam journal of mathematics 2018 paper bib

    Pascal Fernsel, Peter Maass

  16. Adversarial Examples in Modern Machine Learning: A Review. arXiv 2019 paper bib

    Rey Reza Wiyatno, Anqi Xu, Ousmane Dia, Archy de Berker

  17. Algorithms Inspired by Nature: A Survey. arXiv 2019 paper bib

    Pranshu Gupta

  18. An Overview of Privacy in Machine Learning. arXiv 2020 paper bib

    Emiliano De Cristofaro

  19. Are deep learning models superior for missing data imputation in large surveys? Evidence from an empirical comparison. arXiv 2021 paper bib

    Zhenhua Wang, Olanrewaju Akande, Jason Poulos, Fan Li

  20. Certification of embedded systems based on Machine Learning: A survey. arXiv 2021 paper bib

    Guillaume Vidot, Christophe Gabreau, Ileana Ober, Iulian Ober

  21. Class-incremental learning: survey and performance evaluation. arXiv 2020 paper bib

    Marc Masana, Xialei Liu, Bartlomiej Twardowski, Mikel Menta, Andrew D. Bagdanov, Joost van de Weijer

  22. Data and its (dis)contents: A survey of dataset development and use in machine learning research. Patterns 2021 paper bib

    Amandalynne Paullada, Inioluwa Deborah Raji, Emily M. Bender, Emily Denton, Alex Hanna

  23. Generating Artificial Outliers in the Absence of Genuine Ones - a Survey. ACM Trans. Knowl. Discov. Data 2021 paper bib

    Georg Steinbuss, Klemens Böhm

  24. Hierarchical Mixtures-of-Experts for Exponential Family Regression Models with Generalized Linear Mean Functions: A Survey of Approximation and Consistency Results. UAI 1998 paper bib

    Wenxin Jiang, Martin A. Tanner

  25. Hyperbox-based machine learning algorithms: A comprehensive survey. Soft Comput. 2021 paper bib

    Thanh Tung Khuat, Dymitr Ruta, Bogdan Gabrys

  26. Introduction to Core-sets: an Updated Survey. arXiv 2020 paper bib

    Dan Feldman

  27. Laplacian-Based Dimensionality Reduction Including Spectral Clustering, Laplacian Eigenmap, Locality Preserving Projection, Graph Embedding, and Diffusion Map: Tutorial and Survey. arXiv 2021 paper bib

    Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley

  28. Logic Locking at the Frontiers of Machine Learning: A Survey on Developments and Opportunities. VLSI-SoC 2021 paper bib

    Dominik Sisejkovic, Lennart M. Reimann, Elmira Moussavi, Farhad Merchant, Rainer Leupers

  29. Machine Learning at the Network Edge: A Survey. ACM Comput. Surv. 2022 paper bib

    M. G. Sarwar Murshed, Christopher Murphy, Daqing Hou, Nazar Khan, Ganesh Ananthanarayanan, Faraz Hussain

  30. Machine Learning for Spatiotemporal Sequence Forecasting: A Survey. arXiv 2018 paper bib

    Xingjian Shi, Dit-Yan Yeung

  31. Machine Learning in Network Centrality Measures: Tutorial and Outlook. ACM Comput. Surv. 2019 paper bib

    Felipe Grando, Lisandro Zambenedetti Granville, Luís C. Lamb

  32. Machine Learning Testing: Survey, Landscapes and Horizons. IEEE Trans. Software Eng. 2022 paper bib

    Jie M. Zhang, Mark Harman, Lei Ma, Yang Liu

  33. Machine Learning that Matters. ICML 2012 paper bib

    Kiri Wagstaff

  34. Machine Learning with World Knowledge: The Position and Survey. arXiv 2017 paper bib

    Yangqiu Song, Dan Roth

  35. Mean-Field Learning: a Survey. arXiv 2012 paper bib

    Hamidou Tembine, Raúl Tempone, Pedro Vilanova

  36. Multidimensional Scaling, Sammon Mapping, and Isomap: Tutorial and Survey. arXiv 2020 paper bib

    Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley

  37. Multimodal Machine Learning: A Survey and Taxonomy. IEEE Trans. Pattern Anal. Mach. Intell. 2019 paper bib

    Tadas Baltrusaitis, Chaitanya Ahuja, Louis-Philippe Morency

  38. Multi-Objective Multi-Agent Decision Making: A Utility-based Analysis and Survey. AAMAS 2020 paper bib

    Roxana Radulescu, Patrick Mannion, Diederik M. Roijers, Ann Nowé

  39. Rational Kernels: A survey. arXiv 2019 paper bib

    Abhishek Ghose

  40. Statistical Queries and Statistical Algorithms: Foundations and Applications. arXiv 2020 paper bib

    Lev Reyzin

  41. Structure Learning of Probabilistic Graphical Models: A Comprehensive Survey. arXiv 2011 paper bib

    Yang Zhou

  42. Survey & Experiment: Towards the Learning Accuracy. arXiv 2010 paper bib

    Zeyuan Allen Zhu

  43. Survey on Feature Selection. arXiv 2015 paper bib

    Tarek Amr Abdallah, Beatriz de la Iglesia

  44. Survey on Multi-output Learning. IEEE Trans. Neural Networks Learn. Syst. 2020 paper bib

    Donna Xu, Yaxin Shi, Ivor W. Tsang, Yew-Soon Ong, Chen Gong, Xiaobo Shen

  45. Survey: Machine Learning in Production Rendering. arXiv 2020 paper bib

    Shilin Zhu

  46. The Benefits of Population Diversity in Evolutionary Algorithms: A Survey of Rigorous Runtime Analyses. arXiv 2018 paper bib

    Dirk Sudholt

  47. Towards Causal Representation Learning. arXiv 2021 paper bib

    Bernhard Schölkopf, Francesco Locatello, Stefan Bauer, Nan Rosemary Ke, Nal Kalchbrenner, Anirudh Goyal, Yoshua Bengio

  48. Verification for Machine Learning, Autonomy, and Neural Networks Survey. arXiv 2018 paper bib

    Weiming Xiang, Patrick Musau, Ayana A. Wild, Diego Manzanas Lopez, Nathaniel Hamilton, Xiaodong Yang, Joel A. Rosenfeld, Taylor T. Johnson

  49. What Can Knowledge Bring to Machine Learning? - A Survey of Low-shot Learning for Structured Data. arXiv 2021 paper bib

    Yang Hu, Adriane Chapman, Guihua Wen, Wendy Hall

  50. 机器学习的五大类别及其主要算法综述. 软件导刊 2019 paper bib

    李旭然, 丁晓红

Generative Adversarial Networks

  1. A Review of Generative Adversarial Networks in Cancer Imaging: New Applications, New Solutions. arXiv 2021 paper bib

    Richard Osuala, Kaisar Kushibar, Lidia Garrucho, Akis Linardos, Zuzanna Szafranowska, Stefan Klein, Ben Glocker, Oliver Díaz, Karim Lekadir

  2. A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications. arXiv 2020 paper bib

    Jie Gui, Zhenan Sun, Yonggang Wen, Dacheng Tao, Jieping Ye

  3. A Survey on Generative Adversarial Networks: Variants, Applications, and Training. ACM Comput. Surv. 2022 paper bib

    Abdul Jabbar, Xi Li, Bourahla Omar

  4. Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models. arXiv 2021 paper bib

    Sam Bond-Taylor, Adam Leach, Yang Long, Chris G. Willcocks

  5. GAN Computers Generate Arts? A Survey on Visual Arts, Music, and Literary Text Generation using Generative Adversarial Network. arXiv 2021 paper bib

    Sakib Shahriar

  6. GAN Inversion: A Survey. arXiv 2021 paper bib

    Weihao Xia, Yulun Zhang, Yujiu Yang, Jing-Hao Xue, Bolei Zhou, Ming-Hsuan Yang

  7. Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy. ACM Comput. Surv. 2021 paper bib

    Zhengwei Wang, Qi She, Tomás E. Ward

  8. Generative Adversarial Networks in Human Emotion Synthesis: A Review. IEEE Access 2020 paper bib

    Noushin Hajarolasvadi, Miguel Arjona Ramírez, Wesley Beccaro, Hasan Demirel

  9. Generative Adversarial Networks: A Survey Towards Private and Secure Applications. arXiv 2021 paper bib

    Zhipeng Cai, Zuobin Xiong, Honghui Xu, Peng Wang, Wei Li, Yi Pan

  10. Generative Adversarial Networks: An Overview. IEEE Signal Process. Mag. 2018 paper bib

    Antonia Creswell, Tom White, Vincent Dumoulin, Kai Arulkumaran, Biswa Sengupta, Anil A. Bharath

  11. How Generative Adversarial Networks and Their Variants Work: An Overview. ACM Comput. Surv. 2019 paper bib

    Yongjun Hong, Uiwon Hwang, Jaeyoon Yoo, Sungroh Yoon

  12. Stabilizing Generative Adversarial Networks: A Survey. arXiv 2019 paper bib

    Maciej Wiatrak, Stefano V. Albrecht, Andrew Nystrom

Graph Neural Networks

  1. A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications. IEEE Trans. Knowl. Data Eng. 2018 paper bib

    Hongyun Cai, Vincent W. Zheng, Kevin Chen-Chuan Chang

  2. A Comprehensive Survey on Graph Neural Networks. IEEE Trans. Neural Networks Learn. Syst. 2021 paper bib

    Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, Philip S. Yu

  3. A Survey on Graph Neural Networks for Knowledge Graph Completion. arXiv 2020 paper bib

    Siddhant Arora

  4. A Survey on Graph Structure Learning: Progress and Opportunities. arXiv 2021 paper bib

    Yanqiao Zhu, Weizhi Xu, Jinghao Zhang, Yuanqi Du, Jieyu Zhang, Qiang Liu, Carl Yang, Shu Wu

  5. A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources. arXiv 2020 paper bib

    Xiao Wang, Deyu Bo, Chuan Shi, Shaohua Fan, Yanfang Ye, Philip S. Yu

  6. A Survey on The Expressive Power of Graph Neural Networks. arXiv 2020 paper bib

    Ryoma Sato

  7. A Systematic Survey on Deep Generative Models for Graph Generation. arXiv 2020 paper bib

    Xiaojie Guo, Liang Zhao

  8. Adversarial Attack and Defense on Graph Data: A Survey. arXiv 2018 paper bib

    Lichao Sun, Ji Wang, Philip S. Yu, Bo Li

  9. Bridging the Gap between Spatial and Spectral Domains: A Survey on Graph Neural Networks. arXiv 2020 paper bib

    Zhiqian Chen, Fanglan Chen, Lei Zhang, Taoran Ji, Kaiqun Fu, Liang Zhao, Feng Chen, Chang-Tien Lu

  10. Computing Graph Neural Networks: A Survey from Algorithms to Accelerators. ACM Comput. Surv. 2022 paper bib

    Sergi Abadal, Akshay Jain, Robert Guirado, Jorge López-Alonso, Eduard Alarcón

  11. Deep Graph Similarity Learning: A Survey. Data Min. Knowl. Discov. 2021 paper bib

    Guixiang Ma, Nesreen K. Ahmed, Theodore L. Willke, Philip S. Yu

  12. Deep Learning on Graphs: A Survey. IEEE Trans. Knowl. Data Eng. 2022 paper bib

    Ziwei Zhang, Peng Cui, Wenwu Zhu

  13. Explainability in Graph Neural Networks: A Taxonomic Survey. arXiv 2020 paper bib

    Hao Yuan, Haiyang Yu, Shurui Gui, Shuiwang Ji

  14. Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey. arXiv 2020 paper bib

    Joakim Skarding, Bogdan Gabrys, Katarzyna Musial

  15. Graph Embedding Techniques, Applications, and Performance: A Survey. Knowl. Based Syst. 2018 paper bib

    Palash Goyal, Emilio Ferrara

  16. Graph Learning for Combinatorial Optimization: A Survey of State-of-the-Art. Data Sci. Eng. 2021 paper bib

    Yun Peng, Byron Choi, Jianliang Xu

  17. Graph Learning: A Survey. IEEE Trans. Artif. Intell. 2021 paper bib

    Feng Xia, Ke Sun, Shuo Yu, Abdul Aziz, Liangtian Wan, Shirui Pan, Huan Liu

  18. Graph Neural Network for Traffic Forecasting: A Survey. arXiv 2021 paper bib

    Weiwei Jiang, Jiayun Luo

  19. Graph Neural Networks for Natural Language Processing: A Survey. arXiv 2021 paper bib

    Lingfei Wu, Yu Chen, Kai Shen, Xiaojie Guo, Hanning Gao, Shucheng Li, Jian Pei, Bo Long

  20. Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective. IJCAI 2020 paper bib

    Luís C. Lamb, Artur S. d'Avila Garcez, Marco Gori, Marcelo O. R. Prates, Pedro H. C. Avelar, Moshe Y. Vardi

  21. Graph Neural Networks: A Review of Methods and Applications. AI Open 2020 paper bib

    Jie Zhou, Ganqu Cui, Shengding Hu, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, Maosong Sun

  22. Graph Neural Networks: Methods, Applications, and Opportunities. arXiv 2021 paper bib

    Lilapati Waikhom, Ripon Patgiri

  23. Graph Neural Networks: Taxonomy, Advances and Trends. arXiv 2020 paper bib

    Yu Zhou, Haixia Zheng, Xin Huang

  24. Graph Representation Learning: A Survey. APSIPA Transactions on Signal and Information Processing 2020 paper bib

    Fenxiao Chen, Yuncheng Wang, Bin Wang, C.-C. Jay Kuo

  25. Graph Self-Supervised Learning: A Survey. arXiv 2021 paper bib

    Yixin Liu, Shirui Pan, Ming Jin, Chuan Zhou, Feng Xia, Philip S. Yu

  26. Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future. Sensors 2021 paper bib

    David Ahmedt-Aristizabal, Mohammad Ali Armin, Simon Denman, Clinton Fookes, Lars Petersson

  27. Introduction to Graph Neural Networks. Synthesis Lectures on Artificial Intelligence and Machine Learning 2020 paper bib

    Zhiyuan Liu, Jie Zhou

  28. Learning Representations of Graph Data - A Survey. arXiv 2019 paper bib

    Mital Kinderkhedia

  29. Meta-Learning with Graph Neural Networks: Methods and Applications. arXiv 2021 paper bib

    Debmalya Mandal, Sourav Medya, Brian Uzzi, Charu Aggarwal

  30. Representation Learning for Dynamic Graphs: A Survey. J. Mach. Learn. Res. 2020 paper bib

    Seyed Mehran Kazemi, Rishab Goel, Kshitij Jain, Ivan Kobyzev, Akshay Sethi, Peter Forsyth, Pascal Poupart

  31. Robustness of deep learning models on graphs: A survey. AI Open 2021 paper bib

    Jiarong Xu, Junru Chen, Siqi You, Zhiqing Xiao, Yang Yang, Jiangang Lu

  32. Self-Supervised Learning of Graph Neural Networks: A Unified Review. arXiv 2021 paper bib

    Yaochen Xie, Zhao Xu, Zhengyang Wang, Shuiwang Ji

  33. Survey of Image Based Graph Neural Networks. arXiv 2021 paper bib

    Usman Nazir, He Wang, Murtaza Taj

  34. Tackling Graphical NLP problems with Graph Recurrent Networks. arXiv 2019 paper bib

    Linfeng Song

Interpretability and Analysis

  1. A brief survey of visualization methods for deep learning models from the perspective of Explainable AI. macs.hw.ac.uk 2018 paper bib

    Ioannis Chalkiadakis

  2. A Survey of Methods for Explaining Black Box Models. ACM Comput. Surv. 2019 paper bib

    Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, Dino Pedreschi

  3. A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI. IEEE Trans. Neural Networks Learn. Syst. 2021 paper bib

    Erico Tjoa, Cuntai Guan

  4. A Survey on Knowledge integration techniques with Artificial Neural Networks for seq-2-seq/time series models. arXiv 2020 paper bib

    Pramod Vadiraja, Muhammad Ali Chattha

  5. A Survey on Neural Network Interpretability. IEEE Trans. Emerg. Top. Comput. Intell. 2021 paper bib

    Yu Zhang, Peter Tiño, Ales Leonardis, Ke Tang

  6. A Survey on the Explainability of Supervised Machine Learning. J. Artif. Intell. Res. 2021 paper bib

    Nadia Burkart, Marco F. Huber

  7. A Survey on Understanding, Visualizations, and Explanation of Deep Neural Networks. arXiv 2021 paper bib

    Atefeh Shahroudnejad

  8. Benchmarking and Survey of Explanation Methods for Black Box Models. arXiv 2021 paper bib

    Francesco Bodria, Fosca Giannotti, Riccardo Guidotti, Francesca Naretto, Dino Pedreschi, Salvatore Rinzivillo

  9. Causal Interpretability for Machine Learning - Problems, Methods and Evaluation. SIGKDD Explor. 2020 paper bib

    Raha Moraffah, Mansooreh Karami, Ruocheng Guo, Adrienne Raglin, Huan Liu

  10. Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI. Inf. Fusion 2020 paper bib

    Alejandro Barredo Arrieta, Natalia Díaz Rodríguez, Javier Del Ser, Adrien Bennetot, Siham Tabik, Alberto Barbado, Salvador García, Sergio Gil-Lopez, Daniel Molina, Richard Benjamins, Raja Chatila, Francisco Herrera

  11. Explainable Artificial Intelligence Approaches: A Survey. arXiv 2021 paper bib

    Sheikh Rabiul Islam, William Eberle, Sheikh Khaled Ghafoor, Mohiuddin Ahmed

  12. Explainable artificial intelligence: A survey. MIPRO 2018 paper bib

    Filip Karlo Dosilovic, Mario Brcic, Nikica Hlupic

  13. Explainable Automated Fact-Checking: A Survey. COLING 2020 paper bib

    Neema Kotonya, Francesca Toni

  14. Explainable Reinforcement Learning: A Survey. CD-MAKE 2020 paper bib

    Erika Puiutta, Eric M. S. P. Veith

  15. Foundations of Explainable Knowledge-Enabled Systems. Knowledge Graphs for eXplainable Artificial Intelligence 2020 paper bib

    Shruthi Chari, Daniel M. Gruen, Oshani Seneviratne, Deborah L. McGuinness

  16. How convolutional neural networks see the world - A survey of convolutional neural network visualization methods. Math. Found. Comput. 2018 paper bib

    Zhuwei Qin, Fuxun Yu, Chenchen Liu, Xiang Chen

  17. Interpretable Machine Learning - A Brief History, State-of-the-Art and Challenges. PKDD/ECML Workshops 2020 paper bib

    Christoph Molnar, Giuseppe Casalicchio, Bernd Bischl

  18. Machine Learning Interpretability: A Survey on Methods and Metrics. Electronics 2019 paper bib

    Diogo V. Carvalho, Eduardo M. Pereira, Jaime S. Cardoso

  19. On Interpretability of Artificial Neural Networks: A Survey. IEEE Transactions on Radiation and Plasma Medical Sciences 2021 paper bib

    Feng-Lei Fan, Jinjun Xiong, Mengzhou Li, Ge Wang

  20. On the computation of counterfactual explanations - A survey. arXiv 2019 paper bib

    André Artelt, Barbara Hammer

  21. Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey. arXiv 2020 paper bib

    Arun Das, Paul Rad

  22. Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI). IEEE Access 2018 paper bib

    Amina Adadi, Mohammed Berrada

  23. Survey of explainable machine learning with visual and granular methods beyond quasi-explanations. arXiv 2020 paper bib

    Boris Kovalerchuk, Muhammad Aurangzeb Ahmad, Ankur Teredesai

  24. Understanding Neural Networks via Feature Visualization: A survey. Explainable AI 2019 paper bib

    Anh Nguyen, Jason Yosinski, Jeff Clune

  25. Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers. IEEE Trans. Vis. Comput. Graph. 2019 paper bib

    Fred Hohman, Minsuk Kahng, Robert Pienta, Duen Horng Chau

  26. Visual Interpretability for Deep Learning: a Survey. Frontiers Inf. Technol. Electron. Eng. 2018 paper bib

    Quanshi Zhang, Song-Chun Zhu

  27. Visualisation of Pareto Front Approximation: A Short Survey and Empirical Comparisons. CEC 2019 paper bib

    Huiru Gao, Haifeng Nie, Ke Li

  28. When will the mist clear? On the Interpretability of Machine Learning for Medical Applications: a survey. arXiv 2020 paper bib

    Antonio-Jesús Banegas-Luna, Jorge Peña-García, Adrian Iftene, Fiorella Guadagni, Patrizia Ferroni, Noemi Scarpato, Fabio Massimo Zanzotto, Andrés Bueno-Crespo, Horacio Pérez Sánchez

  29. XAI Methods for Neural Time Series Classification: A Brief Review. arXiv 2021 paper bib

    Ilija Simic, Vedran Sabol, Eduardo E. Veas

Knowledge Distillation

  1. A Selective Survey on Versatile Knowledge Distillation Paradigm for Neural Network Models. arXiv 2020 paper bib

    Jeong-Hoe Ku, Jihun Oh, Young-Yoon Lee, Gaurav Pooniwala, SangJeong Lee

  2. Knowledge Distillation: A Survey. Int. J. Comput. Vis. 2021 paper bib

    Jianping Gou, Baosheng Yu, Stephen J. Maybank, Dacheng Tao

Meta Learning

  1. A Comprehensive Overview and Survey of Recent Advances in Meta-Learning. arXiv 2020 paper bib

    Huimin Peng

  2. A Survey of Deep Meta-Learning. Artif. Intell. Rev. 2021 paper bib

    Mike Huisman, Jan N. van Rijn, Aske Plaat

  3. Meta-learning for Few-shot Natural Language Processing: A Survey. arXiv 2020 paper bib

    Wenpeng Yin

  4. Meta-Learning in Neural Networks: A Survey. TPAMI 2021 paper bib

    Timothy Hospedales, Antreas Antoniou, Paul Micaelli, Amos Storkey

  5. Meta-Learning: A Survey. arXiv 2018 paper bib

    Joaquin Vanschoren

Metric Learning

  1. A Survey on Metric Learning for Feature Vectors and Structured Data. arXiv 2013 paper bib

    Aurélien Bellet, Amaury Habrard, Marc Sebban

  2. A Tutorial on Distance Metric Learning: Mathematical Foundations, Algorithms, Experimental Analysis, Prospects and Challenges. Neurocomputing 2021 paper bib

    Juan-Luis Suárez, Salvador García, Francisco Herrera

ML and DL Applications

  1. A Comprehensive Survey on Deep Music Generation: Multi-level Representations, Algorithms, Evaluations, and Future Directions. arXiv 2020 paper bib

    Shulei Ji, Jing Luo, Xinyu Yang

  2. A Comprehensive Survey on Graph Anomaly Detection with Deep Learning. arXiv 2021 paper bib

    Xiaoxiao Ma, Jia Wu, Shan Xue, Jian Yang, Quan Z. Sheng, Hui Xiong

  3. A Comprehensive Survey on Machine Learning Techniques and User Authentication Approaches for Credit Card Fraud Detection. arXiv 2019 paper bib

    Niloofar Yousefi, Marie Alaghband, Ivan Garibay

  4. A guide to deep learning in healthcare. Nature Medicine 2019 paper bib

    Andre Esteva, Alexandre Robicquet, Bharath Ramsundar, Volodymyr Kuleshov, Mark DePristo, Katherine Chou, Claire Cui, Greg Corrado, Sebastian Thrun, Jeff Dean

  5. A Survey of Deep Learning Applications to Autonomous Vehicle Control. IEEE Trans. Intell. Transp. Syst. 2021 paper bib

    Sampo Kuutti, Richard Bowden, Yaochu Jin, Phil Barber, Saber Fallah

  6. A Survey of Deep Learning Techniques for Autonomous Driving. J. Field Robotics 2020 paper bib

    Sorin Mihai Grigorescu, Bogdan Trasnea, Tiberiu T. Cocias, Gigel Macesanu

  7. A Survey of Machine Learning for Computer Architecture and Systems. arXiv 2021 paper bib

    Nan Wu, Yuan Xie

  8. A Survey of Machine Learning Techniques for Detecting and Diagnosing COVID-19 from Imaging. arXiv 2021 paper bib

    Aishwarza Panday, Muhammad Ashad Kabir, Nihad Karim Chowdhury

  9. A Survey on Anomaly Detection for Technical Systems using LSTM Networks. Comput. Ind. 2021 paper bib

    Benjamin Lindemann, Benjamin Maschler, Nada Sahlab, Michael Weyrich

  10. A Survey on Deep Learning-based Non-Invasive Brain Signals:Recent Advances and New Frontiers. Journal of Neural Engineering 2021 paper bib

    Xiang Zhang, Lina Yao, Xianzhi Wang, Jessica Monaghan, David McAlpine, Yu Zhang

  11. A Survey on Machine Learning Applied to Dynamic Physical Systems. arXiv 2020 paper bib

    Sagar Verma

  12. A Survey on Practical Applications of Multi-Armed and Contextual Bandits. arXiv 2019 paper bib

    Djallel Bouneffouf, Irina Rish

  13. A Survey on Spatial and Spatiotemporal Prediction Methods. arXiv 2020 paper bib

    Zhe Jiang

  14. A Survey on the Use of AI and ML for Fighting the COVID-19 Pandemic. arXiv 2020 paper bib

    Muhammad Nazrul Islam, Toki Tahmid Inan, Suzzana Rafi, Syeda Sabrina Akter, Iqbal H. Sarker, A. K. M. Najmul Islam

  15. A Survey on Traffic Signal Control Methods. arXiv 2019 paper bib

    Hua Wei, Guanjie Zheng, Vikash V. Gayah, Zhenhui Li

  16. Aesthetics, Personalization and Recommendation: A survey on Deep Learning in Fashion. arXiv 2021 paper bib

    Wei Gong, Laila Khalid

  17. Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial. IEEE Commun. Surv. Tutorials 2019 paper bib

    Mingzhe Chen, Ursula Challita, Walid Saad, Changchuan Yin, Mérouane Debbah

  18. Classification of Pathological and Normal Gait: A Survey. arXiv 2020 paper bib

    Ryan C. Saxe, Samantha Kappagoda, David K. A. Mordecai

  19. Classification supporting COVID-19 diagnostics based on patient survey data. arXiv 2020 paper bib

    Joanna Henzel, Joanna Tobiasz, Michal Kozielski, Malgorzata Bach, Pawel Foszner, Aleksandra Gruca, Mateusz Kania, Justyna Mika, Anna Papiez, Aleksandra Werner, Joanna Zyla, Jerzy Jaroszewicz, Joanna Polanska, Marek Sikora

  20. Credit card fraud detection using machine learning: A survey. arXiv 2020 paper bib

    Yvan Lucas, Johannes Jurgovsky

  21. Deep Learning for Click-Through Rate Estimation. IJCAI 2021 paper bib

    Weinan Zhang, Jiarui Qin, Wei Guo, Ruiming Tang, Xiuqiang He

  22. Deep Learning for Spatio-Temporal Data Mining: A Survey. arXiv 2019 paper bib

    Senzhang Wang, Jiannong Cao, Philip S. Yu

  23. Deep learning models for predictive maintenance: a survey, comparison, challenges and prospect. arXiv 2020 paper bib

    Oscar Serradilla, Ekhi Zugasti, Urko Zurutuza

  24. Deep Learning-based Spacecraft Relative Navigation Methods: A Survey. arXiv 2021 paper bib

    Jianing Song, Duarte Rondao, Nabil Aouf

  25. DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction. CIKM 2021 paper bib

    Renhe Jiang, Du Yin, Zhaonan Wang, Yizhuo Wang, Jiewen Deng, Hangchen Liu, Zekun Cai, Jinliang Deng, Xuan Song, Ryosuke Shibasaki

  26. Event Prediction in the Big Data Era: A Systematic Survey. ACM Comput. Surv. 2021 paper bib

    Liang Zhao

  27. Fashion Meets Computer Vision: A Survey. ACM Comput. Surv. 2021 paper bib

    Wen-Huang Cheng, Sijie Song, Chieh-Yun Chen, Shintami Chusnul Hidayati, Jiaying Liu

  28. Going Deeper Into Face Detection: A Survey. arXiv 2021 paper bib

    Shervin Minaee, Ping Luo, Zhe Lin, Kevin W. Bowyer

  29. Graph Representation Learning in Biomedicine. arXiv 2021 paper bib

    Michelle M. Li, Kexin Huang, Marinka Zitnik

  30. Graph-based Deep Learning for Communication Networks: A Survey. arXiv 2021 paper bib

    Weiwei Jiang

  31. How Developers Iterate on Machine Learning Workflows - A Survey of the Applied Machine Learning Literature. arXiv 2018 paper bib

    Doris Xin, Litian Ma, Shuchen Song, Aditya G. Parameswaran

  32. Known Operator Learning and Hybrid Machine Learning in Medical Imaging - A Review of the Past, the Present, and the Future. arXiv 2021 paper bib

    Andreas Maier, Harald Köstler, Marco Heisig, Patrick Krauss, Seung Hee Yang

  33. Machine Learning Aided Static Malware Analysis: A Survey and Tutorial. arXiv 2018 paper bib

    Andrii Shalaginov, Sergii Banin, Ali Dehghantanha, Katrin Franke

  34. Machine Learning for Cataract Classification and Grading on Ophthalmic Imaging Modalities: A Survey. arXiv 2020 paper bib

    Xiaoqing Zhang, Jiansheng Fang, Yan Hu, Yanwu Xu, Risa Higashita, Jiang Liu

  35. Machine Learning for Electronic Design Automation: A Survey. ACM Trans. Design Autom. Electr. Syst. 2021 paper bib

    Guyue Huang, Jingbo Hu, Yifan He, Jialong Liu, Mingyuan Ma, Zhaoyang Shen, Juejian Wu, Yuanfan Xu, Hengrui Zhang, Kai Zhong, Xuefei Ning, Yuzhe Ma, Haoyu Yang, Bei Yu, Huazhong Yang, Yu Wang

  36. Machine Learning for Survival Analysis: A Survey. ACM Comput. Surv. 2019 paper bib

    Ping Wang, Yan Li, Chandan K. Reddy

  37. Medical Image Segmentation using 3D Convolutional Neural Networks: A Review. arXiv 2021 paper bib

    S. Niyas, S. J. Pawan, M. Anand Kumar, Jeny Rajan

  38. Physics-Guided Deep Learning for Dynamical Systems: A Survey. arXiv 2021 paper bib

    Rui Wang

  39. Predicting the Future from First Person (Egocentric) Vision: A Survey. arXiv 2021 paper bib

    Ivan Rodin, Antonino Furnari, Dimitrios Mavroedis, Giovanni Maria Farinella

  40. Prediction of neonatal mortality in Sub-Saharan African countries using data-level linkage of multiple surveys. arXiv 2020 paper bib

    Girmaw Abebe Tadesse, Celia Cintas, Skyler Speakman, Komminist Weldemariam

  41. Requirement Engineering Challenges for AI-intense Systems Development. WAIN@ICSE 2021 paper bib

    Hans-Martin Heyn, Eric Knauss, Amna Pir Muhammad, Olof Eriksson, Jennifer Linder, Padmini Subbiah, Shameer Kumar Pradhan, Sagar Tungal

  42. Short-term Traffic Prediction with Deep Neural Networks: A Survey. IEEE Access 2021 paper bib

    Kyungeun Lee, Moonjung Eo, Euna Jung, Yoonjin Yoon, Wonjong Rhee

  43. Should I Raise The Red Flag? A comprehensive survey of anomaly scoring methods toward mitigating false alarms. arXiv 2019 paper bib

    Zahra Zohrevand, Uwe Glässer

  44. The Threat of Adversarial Attacks on Machine Learning in Network Security - A Survey. arXiv 2019 paper bib

    Olakunle Ibitoye, Rana Abou Khamis, Ashraf Matrawy, M. Omair Shafiq

  45. Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey. IEEE Access 2018 paper bib

    Naveed Akhtar, Ajmal S. Mian

  46. Understanding racial bias in health using the Medical Expenditure Panel Survey data. arXiv 2019 paper bib

    Moninder Singh, Karthikeyan Natesan Ramamurthy

  47. Urban flows prediction from spatial-temporal data using machine learning: A survey. arXiv 2019 paper bib

    Peng Xie, Tianrui Li, Jia Liu, Shengdong Du, Xin Yang, Junbo Zhang

  48. Using Deep Learning for Visual Decoding and Reconstruction from Brain Activity: A Review. arXiv 2021 paper bib

    Madison Van Horn

  49. Utilising Graph Machine Learning within Drug Discovery and Development. arXiv 2020 paper bib

    Thomas Gaudelet, Ben Day, Arian R. Jamasb, Jyothish Soman, Cristian Regep, Gertrude Liu, Jeremy B. R. Hayter, Richard Vickers, Charles Roberts, Jian Tang, David Roblin, Tom L. Blundell, Michael M. Bronstein, Jake P. Taylor-King

Model Compression and Acceleration

  1. A Survey of Model Compression and Acceleration for Deep Neural Networks. arXiv 2017 paper bib

    Yu Cheng, Duo Wang, Pan Zhou, Tao Zhang

  2. A Survey of Quantization Methods for Efficient Neural Network Inference. arXiv 2021 paper bib

    Amir Gholami, Sehoon Kim, Zhen Dong, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer

  3. A Survey on Deep Neural Network Compression: Challenges, Overview, and Solutions. arXiv 2020 paper bib

    Rahul Mishra, Hari Prabhat Gupta, Tanima Dutta

  4. A Survey on GAN Acceleration Using Memory Compression Technique. arXiv 2021 paper bib

    Dina Tantawy, Mohamed Zahran, Amr Wassal

  5. A Survey on Methods and Theories of Quantized Neural Networks. arXiv 2018 paper bib

    Yunhui Guo

  6. An Overview of Neural Network Compression. arXiv 2020 paper bib

    James O'Neill

  7. Compression of Deep Learning Models for Text: A Survey. ACM Trans. Knowl. Discov. Data 2022 paper bib

    Manish Gupta, Puneet Agrawal

  8. Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and Better. arXiv 2021 paper bib

    Gaurav Menghani

  9. Pruning Algorithms to Accelerate Convolutional Neural Networks for Edge Applications: A Survey. arXiv 2020 paper bib

    Jiayi Liu, Samarth Tripathi, Unmesh Kurup, Mohak Shah

  10. Pruning and Quantization for Deep Neural Network Acceleration: A Survey. arXiv 2021 paper bib

    Tailin Liang, John Glossner, Lei Wang, Shaobo Shi

  11. Survey of Machine Learning Accelerators. HPEC 2020 paper bib

    Albert Reuther, Peter Michaleas, Michael Jones, Vijay Gadepally, Siddharth Samsi, Jeremy Kepner

Multi-Label Learning

  1. A Review on Multi-Label Learning Algorithms. IEEE Trans. Knowl. Data Eng. 2014 paper bib

    Min-Ling Zhang, Zhi-Hua Zhou

  2. Multi-Label Classification: An Overview. Int. J. Data Warehous. Min. 2007 paper bib

    Grigorios Tsoumakas, Ioannis Katakis

  3. Multi-label learning: a review of the state of the art and ongoing research. WIREs Data Mining Knowl. Discov. 2014 paper bib

    Eva Lucrecia Gibaja Galindo, Sebastián Ventura

  4. The Emerging Trends of Multi-Label Learning. arXiv 2020 paper bib

    Weiwei Liu, Xiaobo Shen, Haobo Wang, Ivor W. Tsang

Multi-Task and Multi-View Learning

  1. A brief review on multi-task learning. Multim. Tools Appl. 2018 paper bib

    Kim-Han Thung, Chong-Yaw Wee

  2. A Survey on Multi-Task Learning. IEEE Trans. Knowl. Data Eng. 2021 paper bib

    Yu Zhang, Qiang Yang

  3. A Survey on Multi-view Learning. arXiv 2013 paper bib

    Chang Xu, Dacheng Tao, Chao Xu

  4. An overview of multi-task learning. National Science Review 2017 paper bib

    Yu Zhang, Qiang Yang

  5. An Overview of Multi-Task Learning in Deep Neural Networks. arXiv 2017 paper bib

    Sebastian Ruder

  6. Multi-Task Learning for Dense Prediction Tasks: A Survey. TPAMI 2021 paper bib

    Simon Vandenhende, Stamatios Georgoulis, Wouter Van Gansbeke, Marc Proesmans, Dengxin Dai, Luc Van Gool

  7. Multi-task learning for natural language processing in the 2020s: where are we going?. Pattern Recognit. Lett. 2020 paper bib

    Joseph Worsham, Jugal Kalita

  8. Multi-Task Learning with Deep Neural Networks: A Survey. arXiv 2020 paper bib

    Michael Crawshaw

Online Learning

  1. A Survey of Algorithms and Analysis for Adaptive Online Learning. J. Mach. Learn. Res. 2017 paper bib

    H. Brendan McMahan

  2. Online Continual Learning in Image Classification: An Empirical Survey. Neurocomputing 2022 paper bib

    Zheda Mai, Ruiwen Li, Jihwan Jeong, David Quispe, Hyunwoo Kim, Scott Sanner

  3. Online Learning: A Comprehensive Survey. Neurocomputing 2021 paper bib

    Steven C. H. Hoi, Doyen Sahoo, Jing Lu, Peilin Zhao

  4. Preference-based Online Learning with Dueling Bandits: A Survey. J. Mach. Learn. Res. 2021 paper bib

    Viktor Bengs, Róbert Busa-Fekete, Adil El Mesaoudi-Paul, Eyke Hüllermeier

Optimization

  1. A Survey of Optimization Methods from a Machine Learning Perspective. IEEE Trans. Cybern. 2020 paper bib

    Shiliang Sun, Zehui Cao, Han Zhu, Jing Zhao

  2. A Systematic and Meta-analysis Survey of Whale Optimization Algorithm. Comput. Intell. Neurosci. 2019 paper bib

    Hardi M. Mohammed, Shahla U. Umar, Tarik A. Rashid

  3. An overview of gradient descent optimization algorithms. arXiv 2016 paper bib

    Sebastian Ruder

  4. Convex Optimization Overview. citeseerx 2008 paper bib

    Zico Kolter, Honglak Lee

  5. Evolutionary Multitask Optimization: a Methodological Overview, Challenges and Future Research Directions. arXiv 2021 paper bib

    Eneko Osaba, Aritz D. Martinez, Javier Del Ser

  6. Gradient Boosting Machine: A Survey. arXiv 2019 paper bib

    Zhiyuan He, Danchen Lin, Thomas Lau, Mike Wu

  7. Investigating Bi-Level Optimization for Learning and Vision from a Unified Perspective: A Survey and Beyond. arXiv 2021 paper bib

    Risheng Liu, Jiaxin Gao, Jin Zhang, Deyu Meng, Zhouchen Lin

  8. Learning Combinatorial Optimization on Graphs: A Survey with Applications to Networking. IEEE Access 2020 paper bib

    Natalia Vesselinova, Rebecca Steinert, Daniel F. Perez-Ramirez, Magnus Boman

  9. Nature-Inspired Optimization Algorithms: Research Direction and Survey. arXiv 2021 paper bib

    Rohit Kumar Sachan, Dharmender Singh Kushwaha

  10. Optimization for deep learning: theory and algorithms. arXiv 2019 paper bib

    Ruoyu Sun

  11. Optimization Problems for Machine Learning: A Survey. Eur. J. Oper. Res. 2021 paper bib

    Claudio Gambella, Bissan Ghaddar, Joe Naoum-Sawaya

  12. Particle Swarm Optimization: A survey of historical and recent developments with hybridization perspectives. Mach. Learn. Knowl. Extr. 2019 paper bib

    Saptarshi Sengupta, Sanchita Basak, Richard A. Peters

  13. Why Do Local Methods Solve Nonconvex Problems?. Beyond the Worst-Case Analysis of Algorithms 2020 paper bib

    Tengyu Ma

Semi-Supervised, Weakly-Supervised and Unsupervised Learning

  1. A brief introduction to weakly supervised learning. National Science Review 2017 paper bib

    Zhi-Hua Zhou

  2. A Survey of Unsupervised Dependency Parsing. COLING 2020 paper bib

    Wenjuan Han, Yong Jiang, Hwee Tou Ng, Kewei Tu

  3. A Survey on Deep Semi-supervised Learning. arXiv 2021 paper bib

    Xiangli Yang, Zixing Song, Irwin King, Zenglin Xu

  4. A survey on Semi-, Self- and Unsupervised Learning for Image Classification. IEEE Access 2021 paper bib

    Lars Schmarje, Monty Santarossa, Simon-Martin Schröder, Reinhard Koch

  5. A Survey on Semi-Supervised Learning Techniques. IJCTT 2014 paper bib

    V. Jothi Prakash, Dr. L.M. Nithya

  6. Deep Learning for Weakly-Supervised Object Detection and Object Localization: A Survey. arXiv 2021 paper bib

    Feifei Shao, Long Chen, Jian Shao, Wei Ji, Shaoning Xiao, Lu Ye, Yueting Zhuang, Jun Xiao

  7. Graph-based Semi-supervised Learning: A Comprehensive Review. arXiv 2021 paper bib

    Zixing Song, Xiangli Yang, Zenglin Xu, Irwin King

  8. Improvability Through Semi-Supervised Learning: A Survey of Theoretical Results. arXiv 2019 paper bib

    Alexander Mey, Marco Loog

  9. Learning from positive and unlabeled data: a survey. Mach. Learn. 2020 paper bib

    Jessa Bekker, Jesse Davis

  10. Unsupervised Cross-Lingual Representation Learning. ACL 2019 paper bib

    Sebastian Ruder, Anders Søgaard, Ivan Vulic

Transfer Learning

  1. A Comprehensive Survey on Transfer Learning. Proc. IEEE 2021 paper bib

    Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan, Dongbo Xi, Yongchun Zhu, Hengshu Zhu, Hui Xiong, Qing He

  2. A Survey of Unsupervised Deep Domain Adaptation. ACM Trans. Intell. Syst. Technol. 2020 paper bib

    Garrett Wilson, Diane J. Cook

  3. A Survey on Deep Transfer Learning. ICANN 2018 paper bib

    Chuanqi Tan, Fuchun Sun, Tao Kong, Wenchang Zhang, Chao Yang, Chunfang Liu

  4. A survey on domain adaptation theory: learning bounds and theoretical guarantees. arXiv 2020 paper bib

    Ievgen Redko, Emilie Morvant, Amaury Habrard, Marc Sebban, Younès Bennani

  5. A Survey on Negative Transfer. arXiv 2020 paper bib

    Wen Zhang, Lingfei Deng, Lei Zhang, Dongrui Wu

  6. A Survey on Transfer Learning. IEEE Trans. Knowl. Data Eng. 2010 paper bib

    Sinno Jialin Pan, Qiang Yang

  7. A Survey on Transfer Learning in Natural Language Processing. arXiv 2020 paper bib

    Zaid Alyafeai, Maged Saeed AlShaibani, Irfan Ahmad

  8. Evolution of transfer learning in natural language processing. arXiv 2019 paper bib

    Aditya Malte, Pratik Ratadiya

  9. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. J. Mach. Learn. Res. 2020 paper bib

    Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu

  10. Neural Unsupervised Domain Adaptation in NLP - A Survey. COLING 2020 paper bib

    Alan Ramponi, Barbara Plank

  11. Transfer Adaptation Learning: A Decade Survey. arXiv 2019 paper bib

    Lei Zhang, Xinbo Gao

  12. Transfer Learning for Reinforcement Learning Domains: A Survey. J. Mach. Learn. Res. 2009 paper bib

    Matthew E. Taylor, Peter Stone

  13. Transfer Learning in Deep Reinforcement Learning: A Survey. arXiv 2020 paper bib

    Zhuangdi Zhu, Kaixiang Lin, Jiayu Zhou

Trustworthy Machine Learning

  1. A Survey of Privacy Attacks in Machine Learning. arXiv 2020 paper bib

    Maria Rigaki, Sebastian Garcia

  2. A survey of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and interpretability. Comput. Sci. Rev. 2020 paper bib

    Xiaowei Huang, Daniel Kroening, Wenjie Ruan, James Sharp, Youcheng Sun, Emese Thamo, Min Wu, Xinping Yi

  3. A Survey on Bias and Fairness in Machine Learning. ACM Comput. Surv. 2021 paper bib

    Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, Aram Galstyan

  4. Backdoor Learning: A Survey. arXiv 2020 paper bib

    Yiming Li, Baoyuan Wu, Yong Jiang, Zhifeng Li, Shu-Tao Xia

  5. Differential Privacy and Machine Learning: a Survey and Review. arXiv 2014 paper bib

    Zhanglong Ji, Zachary Chase Lipton, Charles Elkan

  6. Fairness in Machine Learning: A Survey. arXiv 2020 paper bib

    Simon Caton, Christian Haas

  7. Local Differential Privacy and Its Applications: A Comprehensive Survey. arXiv 2020 paper bib

    Mengmeng Yang, Lingjuan Lyu, Jun Zhao, Tianqing Zhu, Kwok-Yan Lam

  8. Practical Machine Learning Safety: A Survey and Primer. arXiv 2021 paper bib

    Sina Mohseni, Haotao Wang, Zhiding Yu, Chaowei Xiao, Zhangyang Wang, Jay Yadawa

  9. Privacy in Deep Learning: A Survey. arXiv 2020 paper bib

    Fatemehsadat Mireshghallah, Mohammadkazem Taram, Praneeth Vepakomma, Abhishek Singh, Ramesh Raskar, Hadi Esmaeilzadeh

  10. Technology Readiness Levels for Machine Learning Systems. arXiv 2021 paper bib

    Alexander Lavin, Ciarán M. Gilligan-Lee, Alessya Visnjic, Siddha Ganju, Dava Newman, Sujoy Ganguly, Danny Lange, Atilim Günes Baydin, Amit Sharma, Adam Gibson, Yarin Gal, Eric P. Xing, Chris Mattmann, James Parr

  11. The Creation and Detection of Deepfakes: A Survey. ACM Comput. Surv. 2021 paper bib

    Yisroel Mirsky, Wenke Lee

  12. Tutorial: Safe and Reliable Machine Learning. arXiv 2019 paper bib

    Suchi Saria, Adarsh Subbaswamy

  13. When Machine Learning Meets Privacy: A Survey and Outlook. ACM Comput. Surv. 2021 paper bib

    Bo Liu, Ming Ding, Sina Shaham, Wenny Rahayu, Farhad Farokhi, Zihuai Lin

  14. 机器学习模型安全与隐私研究综述. 软件学报 2021 paper bib

    纪守领, 杜天宇, 李进锋, 沈超, 李博

Team Members

The project is maintained by

Ziyang Wang, Shuhan Zhou, Nuo Xu, Bei Li, Yinqiao Li, Quan Du, Tong Xiao, and Jingbo Zhu

Natural Language Processing Lab., School of Computer Science and Engineering, Northeastern University

NiuTrans Research

Please feel free to contact us if you have any questions (wangziyang [at] stumail.neu.edu.cn or libei_neu [at] outlook.com).

Acknowledge

We would like to thank the people who have contributed to this project. They are

Xin Zeng, Laohu Wang, Chenglong Wang, Xiaoqian Liu, Xuanjun Zhou, Jingnan Zhang, Yongyu Mu, Zefan Zhou, Yanhong Jiang, Xinyang Zhu, Xingyu Liu, Dong Bi, Ping Xu, Zijian Li, Fengning Tian, Hui Liu, Kai Feng, Yuhao Zhang, Chi Hu, Di Yang, Lei Zheng, Hexuan Chen, Zeyang Wang, Tengbo Liu, Xia Meng, Weiqiao Shan, Tao Zhou, Runzhe Cao, Yingfeng Luo, Binghao Wei, Wandi Xu, Yan Zhang, Yichao Wang, Mengyu Ma, Zihao Liu

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A collection of 700+ survey papers on Natural Language Processing (NLP) and Machine Learning (ML)

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