Startups

• 机器学习、深度学习、计算机视觉、大数据创业公司 - Startups in AI

Deep Reinforcement Learning

• David Silver. "Tutorial: Deep Reinforcement Learning." ICML 2016.
• David Silver’s course. "Reinforcement Learning". 2015.
• Bahdanau, Dzmitry, Philemon Brakel, Kelvin Xu, Anirudh Goyal, Ryan Lowe, Joelle Pineau, Aaron Courville, and Yoshua Bengio. "An Actor-Critic Algorithm for Sequence Prediction." arXiv preprint arXiv:1607.07086 (2016).
• Li, Jiwei, Will Monroe, Alan Ritter, and Dan Jurafsky. "Deep Reinforcement Learning for Dialogue Generation." arXiv preprint arXiv:1606.01541 (2016).

Text Summarization

• Ryang, Seonggi, and Takeshi Abekawa. "Framework of automatic text summarization using reinforcement learning." In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 256-265. Association for Computational Linguistics, 2012. [not neural-based methods]
• King, Ben, Rahul Jha, Tyler Johnson, Vaishnavi Sundararajan, and Clayton Scott. "Experiments in Automatic Text Summarization Using Deep Neural Networks." Machine Learning (2011).
• Liu, Yan, Sheng-hua Zhong, and Wenjie Li. "Query-Oriented Multi-Document Summarization via Unsupervised Deep Learning." AAAI. 2012.
• Rioux, Cody, Sadid A. Hasan, and Yllias Chali. "Fear the REAPER: A System for Automatic Multi-Document Summarization with Reinforcement Learning." In EMNLP, pp. 681-690. 2014.[not neural-based methods]
• PadmaPriya, G., and K. Duraiswamy. "An Approach For Text Summarization Using Deep Learning Algorithm." Journal of Computer Science 10, no. 1 (2013): 1-9.
• Denil, Misha, Alban Demiraj, and Nando de Freitas. "Extraction of Salient Sentences from Labelled Documents." arXiv preprint arXiv:1412.6815 (2014).
• Kågebäck, Mikael, et al. "Extractive summarization using continuous vector space models." Proceedings of the 2nd Workshop on Continuous Vector Space Models and their Compositionality (CVSC)@ EACL. 2014.
• Denil, Misha, Alban Demiraj, Nal Kalchbrenner, Phil Blunsom, and Nando de Freitas. "Modelling, Visualising and Summarising Documents with a Single Convolutional Neural Network." arXiv preprint arXiv:1406.3830 (2014).
• Cao, Ziqiang, Furu Wei, Li Dong, Sujian Li, and Ming Zhou. "Ranking with Recursive Neural Networks and Its Application to Multi-document Summarization." (AAAI'2015).
• Fei Liu, Jeffrey Flanigan, Sam Thomson, Norman Sadeh, and Noah A. Smith. "Toward Abstractive Summarization Using Semantic Representations." NAACL 2015
• Wenpeng Yin， Yulong Pei. "Optimizing Sentence Modeling and Selection for Document Summarization." IJCAI 2015
• He, Zhanying, Chun Chen, Jiajun Bu, Can Wang, Lijun Zhang, Deng Cai, and Xiaofei He. "Document Summarization Based on Data Reconstruction." In AAAI. 2012.
• Liu, He, Hongliang Yu, and Zhi-Hong Deng. "Multi-Document Summarization Based on Two-Level Sparse Representation Model." In Twenty-Ninth AAAI Conference on Artificial Intelligence. 2015.
• Jin-ge Yao, Xiaojun Wan, Jianguo Xiao. "Compressive Document Summarization via Sparse Optimization." IJCAI 2015
• Piji Li, Lidong Bing, Wai Lam, Hang Li, and Yi Liao. "Reader-Aware Multi-Document Summarization via Sparse Coding." IJCAI 2015.
• Lopyrev, Konstantin. "Generating News Headlines with Recurrent Neural Networks." arXiv preprint arXiv:1512.01712 (2015). [The first paragraph as document.]
• Alexander M. Rush, Sumit Chopra, Jason Weston. "A Neural Attention Model for Abstractive Sentence Summarization." EMNLP 2015. [sentence compression]
• Hu, Baotian, Qingcai Chen, and Fangze Zhu. "LCSTS: a large scale chinese short text summarization dataset." arXiv preprint arXiv:1506.05865 (2015).
• Gulcehre, Caglar, Sungjin Ahn, Ramesh Nallapati, Bowen Zhou, and Yoshua Bengio. "Pointing the Unknown Words." arXiv preprint arXiv:1603.08148 (2016).
• Nallapati, Ramesh, Bing Xiang, and Bowen Zhou. "Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond." arXiv preprint arXiv:1602.06023 (2016). [sentence compression]
• Sumit Chopra, Alexander M. Rush and Michael Auli. "Abstractive Sentence Summarization with Attentive Recurrent Neural Networks" NAACL 2016.
• Jiatao Gu, Zhengdong Lu, Hang Li, Victor O.K. Li. "Incorporating Copying Mechanism in Sequence-to-Sequence Learning." ACL. (2016)
• Jianpeng Cheng, Mirella Lapata. "Neural Summarization by Extracting Sentences and Words". ACL. (2016)
• Ziqiang Cao, Wenjie Li, Sujian Li, Furu Wei. "AttSum: Joint Learning of Focusing and Summarization with Neural Attention". arXiv:1604.00125 (2016)
• Ayana, Shiqi Shen, Zhiyuan Liu, Maosong Sun. "Neural Headline Generation with Minimum Risk Training". arXiv:1604.01904 (2016)
• Qian Chen, Xiaodan Zhu, Zhenhua Ling, Si Wei and Hui Jiang. "A Neural Network for Document Summarization." IJCAI 2016.
• Wang, Lu, and Wang Ling. "Neural Network-Based Abstract Generation for Opinions and Arguments." NAACL 2016.
• Yishu Miao, Phil Blunsom. "Language as a Latent Variable: Discrete Generative Models for Sentence Compression." EMNLP 2016.

Opinion Summarization

• Wu, Haibing, Yiwei Gu, Shangdi Sun, and Xiaodong Gu. "Aspect-based Opinion Summarization with Convolutional Neural Networks." arXiv preprint arXiv:1511.09128 (2015).
• Irsoy, Ozan, and Claire Cardie. "Opinion Mining with Deep Recurrent Neural Networks." In EMNLP, pp. 720-728. 2014.

Reading Comprehension

• Hermann, Karl Moritz, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. "Teaching machines to read and comprehend." In Advances in Neural Information Processing Systems, pp. 1693-1701. 2015.
• Hill, Felix, Antoine Bordes, Sumit Chopra, and Jason Weston. "The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations." arXiv preprint arXiv:1511.02301 (2015).
• Kadlec, Rudolf, Martin Schmid, Ondrej Bajgar, and Jan Kleindienst. "Text Understanding with the Attention Sum Reader Network." arXiv preprint arXiv:1603.01547 (2016).
• Chen, Danqi, Jason Bolton, and Christopher D. Manning. "A thorough examination of the cnn/daily mail reading comprehension task." arXiv preprint arXiv:1606.02858 (2016).
• Dhingra, Bhuwan, Hanxiao Liu, William W. Cohen, and Ruslan Salakhutdinov. "Gated-Attention Readers for Text Comprehension." arXiv preprint arXiv:1606.01549 (2016).
• Sordoni, Alessandro, Phillip Bachman, and Yoshua Bengio. "Iterative Alternating Neural Attention for Machine Reading." arXiv preprint arXiv:1606.02245 (2016).
• Trischler, Adam, Zheng Ye, Xingdi Yuan, and Kaheer Suleman. "Natural Language Comprehension with the EpiReader." arXiv preprint arXiv:1606.02270 (2016).
• Yiming Cui, Zhipeng Chen, Si Wei, Shijin Wang, Ting Liu, Guoping Hu. "Attention-over-Attention Neural Networks for Reading Comprehension." arXiv preprint arXiv:1607.04423 (2016).
• Yiming Cui, Ting Liu, Zhipeng Chen, Shijin Wang, Guoping Hu. "Consensus Attention-based Neural Networks for Chinese Reading Comprehension." arXiv preprint arXiv:1607.02250 (2016).
• Daniel Hewlett, Alexandre Lacoste, Llion Jones, Illia Polosukhin, Andrew Fandrianto, Jay Han, Matthew Kelcey and David Berthelot. "WIKIREADING: A Novel Large-scale Language Understanding Task over Wikipedia." ACL (2016). pp. 1535-1545.

Sentence Modelling

• Kalchbrenner, Nal, Edward Grefenstette, and Phil Blunsom. "A convolutional neural network for modelling sentences." arXiv preprint arXiv:1404.2188 (2014).
• Kim, Yoon. "Convolutional neural networks for sentence classification." arXiv preprint arXiv:1408.5882 (2014).
• Le, Quoc V., and Tomas Mikolov. "Distributed representations of sentences and documents." arXiv preprint arXiv:1405.4053 (2014).
• Yang, Zichao, Diyi Yang, Chris Dyer, Xiaodong He, Alex Smola, and Eduard Hovy. "Hierarchical Attention Networks for Document Classification." In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2016.

Reasoning

• Peng, Baolin, Zhengdong Lu, Hang Li, and Kam-Fai Wong. "Towards Neural Network-based Reasoning." arXiv preprint arXiv:1508.05508 (2015).

Knowledge Engine

• Lin, Yankai, Shiqi Shen, Zhiyuan Liu, Huanbo Luan, and Maosong Sun. "Neural Relation Extraction with Selective Attention over Instances." ACL (2016)

Memory Networks

• Graves, Alex, Greg Wayne, and Ivo Danihelka. "Neural turing machines." arXiv preprint arXiv:1410.5401 (2014).
• Weston, Jason, Sumit Chopra, and Antoine Bordes. "Memory networks." ICLR (2014).
• Sukhbaatar, Sainbayar, Jason Weston, and Rob Fergus. "End-to-end memory networks." In Advances in neural information processing systems, pp. 2440-2448. 2015.
• Weston, Jason, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, and Tomas Mikolov. "Towards ai-complete question answering: A set of prerequisite toy tasks." arXiv preprint arXiv:1502.05698 (2015).
• Bordes, Antoine, Nicolas Usunier, Sumit Chopra, and Jason Weston. "Large-scale simple question answering with memory networks." arXiv preprint arXiv:1506.02075 (2015).
• Kumar, Ankit, Ozan Irsoy, Jonathan Su, James Bradbury, Robert English, Brian Pierce, Peter Ondruska, Ishaan Gulrajani, and Richard Socher. "Ask me anything: Dynamic memory networks for natural language processing." arXiv preprint arXiv:1506.07285 (2015).
• Dodge, Jesse, Andreea Gane, Xiang Zhang, Antoine Bordes, Sumit Chopra, Alexander Miller, Arthur Szlam, and Jason Weston. "Evaluating prerequisite qualities for learning end-to-end dialog systems." arXiv preprint arXiv:1511.06931 (2015).
• Hill, Felix, Antoine Bordes, Sumit Chopra, and Jason Weston. "The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations." arXiv preprint arXiv:1511.02301 (2015).
• Weston, Jason. "Dialog-based Language Learning." arXiv preprint arXiv:1604.06045 (2016).
• Bordes, Antoine, and Jason Weston. "Learning End-to-End Goal-Oriented Dialog." arXiv preprint arXiv:1605.07683 (2016).
• Chandar, Sarath, Sungjin Ahn, Hugo Larochelle, Pascal Vincent, Gerald Tesauro, and Yoshua Bengio. "Hierarchical Memory Networks." arXiv preprint arXiv:1605.07427 (2016).
• Jason Weston."Memory Networks for Language Understanding." ICML Tutorial 2016
• Tang, Yaohua, Fandong Meng, Zhengdong Lu, Hang Li, and Philip LH Yu. "Neural Machine Translation with External Phrase Memory." arXiv preprint arXiv:1606.01792 (2016).
• Wang, Mingxuan, Zhengdong Lu, Hang Li, and Qun Liu. "Memory-enhanced Decoder for Neural Machine Translation." arXiv preprint arXiv:1606.02003 (2016).

Neural Structures

• Srivastava, Rupesh Kumar, Klaus Greff, and Jürgen Schmidhuber. "Highway networks." arXiv preprint arXiv:1505.00387 (2015).
• Srivastava, Rupesh K., Klaus Greff, and Jürgen Schmidhuber. "Training very deep networks." In Advances in Neural Information Processing Systems, pp. 2368-2376. 2015.
• Vinyals, Oriol, Meire Fortunato, and Navdeep Jaitly. "Pointer networks." In Advances in Neural Information Processing Systems, pp. 2692-2700. 2015.
• Rasmus, Antti, Mathias Berglund, Mikko Honkala, Harri Valpola, and Tapani Raiko. "Semi-supervised learning with ladder networks." In Advances in Neural Information Processing Systems, pp. 3546-3554. 2015.
• He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. "Deep Residual Learning for Image Recognition." arXiv preprint arXiv:1512.03385 (2015).
• He, Kaiming. "Tutorial: Deep Residual Networks: Deep Learning Gets Way Deeper." ICML 2016 tutorial.
• Courbariaux, Matthieu, and Yoshua Bengio. "Binarynet: Training deep neural networks with weights and activations constrained to+ 1 or-1." arXiv preprint arXiv:1602.02830 (2016).
• Jiatao Gu, Zhengdong Lu, Hang Li, Victor O.K. Li. "Incorporating Copying Mechanism in Sequence-to-Sequence Learning." ACL (2016)
• Gulcehre, Caglar, Sungjin Ahn, Ramesh Nallapati, Bowen Zhou, and Yoshua Bengio. "Pointing the Unknown Words." arXiv preprint arXiv:1603.08148 (2016).
• Andreas, Jacob, Marcus Rohrbach, Trevor Darrell, and Dan Klein. "Learning to compose neural networks for question answering." NAACL 2016.
• Julian Georg Zilly, Rupesh Kumar Srivastava, Jan Koutník, Jürgen Schmidhuber. "Recurrent Highway Networks." arXiv preprint arXiv:1607.03474 (2016).
• 【VAE】:
• Rezende, Danilo Jimenez, Shakir Mohamed, and Daan Wierstra. "Stochastic backpropagation and approximate inference in deep generative models." arXiv preprint arXiv:1401.4082 (2014).
• Kingma, Diederik P., and Max Welling. "Auto-encoding variational bayes." arXiv preprint arXiv:1312.6114 (2013).
• Fabius, Otto, and Joost R. van Amersfoort. "Variational recurrent auto-encoders." arXiv preprint arXiv:1412.6581 (2014).
• Bayer, Justin, and Christian Osendorfer. "Learning stochastic recurrent networks." arXiv preprint arXiv:1411.7610 (2014).
• Bowman, Samuel R., Luke Vilnis, Oriol Vinyals, Andrew M. Dai, Rafal Jozefowicz, and Samy Bengio. "Generating sentences from a continuous space." arXiv preprint arXiv:1511.06349 (2015).
• Gregor, Karol, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, and Daan Wierstra. "DRAW: A recurrent neural network for image generation." arXiv preprint arXiv:1502.04623 (2015).
• Makhzani, Alireza, Jonathon Shlens, Navdeep Jaitly, and Ian Goodfellow. "Adversarial autoencoders." arXiv preprint arXiv:1511.05644 (2015).
• Johnson, Matthew J., David Duvenaud, Alexander B. Wiltschko, Sandeep R. Datta, and Ryan P. Adams. "Composing graphical models with neural networks for structured representations and fast inference." arXiv preprint arXiv:1603.06277 (2016).
• Doersch, Carl. "Tutorial on Variational Autoencoders." arXiv preprint arXiv:1606.05908 (2016).
• Chung, Junyoung, Kyle Kastner, Laurent Dinh, Kratarth Goel, Aaron C. Courville, and Yoshua Bengio. "A recurrent latent variable model for sequential data." In Advances in neural information processing systems, pp. 2980-2988. 2015.
• Eslami, S. M., Nicolas Heess, Theophane Weber, Yuval Tassa, Koray Kavukcuoglu, and Geoffrey E. Hinton. "Attend, Infer, Repeat: Fast Scene Understanding with Generative Models." arXiv preprint arXiv:1603.08575 (2016).
• 【GAN】:
• Goodfellow, Ian, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. "Generative adversarial nets." In Advances in Neural Information Processing Systems, pp. 2672-2680. 2014
• Radford, Alec, Luke Metz, and Soumith Chintala. "Unsupervised representation learning with deep convolutional generative adversarial networks." arXiv preprint arXiv:1511.06434 (2015).
• Denton, Emily L., Soumith Chintala, and Rob Fergus. "Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks." In Advances in neural information processing systems, pp. 1486-1494. 2015.
• Dosovitskiy, Alexey, Jost Tobias Springenberg, and Thomas Brox. "Learning to generate chairs with convolutional neural networks." In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1538-1546. 2015.
• Mathieu, Michael, Camille Couprie, and Yann LeCun. "Deep multi-scale video prediction beyond mean square error." arXiv preprint arXiv:1511.05440 (2015).
• Salimans, Tim, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. "Improved Techniques for Training GANs." arXiv preprint arXiv:1606.03498 (2016).
• Chen, Xi, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel. "InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets." arXiv preprint arXiv:1606.03657 (2016).
• Im, Daniel Jiwoong, Chris Dongjoo Kim, Hui Jiang, and Roland Memisevic. "Generating images with recurrent adversarial networks." arXiv preprint arXiv:1602.05110 (2016).
• Yu, Lantao, Weinan Zhang, Jun Wang, and Yong Yu. "SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient." arXiv preprint arXiv:1609.05473 (2016).

Music Generation

• Using machine learning to generate music

Computational Biology

• Awesome DeepBio by Gökçen Eraslan

GO

• Silver, David, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser et al. "Mastering the game of Go with deep neural networks and tree search." Nature 529, no. 7587 (2016): 484-489.
• Tian, Yuandong, and Yan Zhu. "Better Computer Go Player with Neural Network and Long-term Prediction." arXiv preprint arXiv:1511.06410 (2015).

Stock Prediction

• Xiao Ding, Yue Zhang, Ting Liu, Junwen Duan. "Deep Learning for Event-Driven Stock Prediction". IJCAI 2015.
• Si, Jianfeng, Arjun Mukherjee, Bing Liu, Sinno Jialin Pan, Qing Li, and Huayi Li. "Exploiting Social Relations and Sentiment for Stock Prediction." EMNLP 2014.
• Ding, Xiao, Yue Zhang, Ting Liu, and Junwen Duan. "Using Structured Events to Predict Stock Price Movement: An Empirical Investigation." EMNLP 2014.
• Bollen, Johan, Huina Mao, and Xiaojun Zeng. "Twitter mood predicts the stock market." Journal of Computational Science 2, no. 1 (2011): 1-8.
• Hengjian Jia. "Investigation Into The Effectiveness Of Long Short Term Memory Networks For Stock Price Prediction." arXiv:1603.07893. (2016)