General: Deep Learning Methods

• {Cho14} Kyunghyun Cho, Bart van Merrienboer, Çaglar Gülçehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, Yoshua Bengio. Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. Proc. of EMNLP, 2014. GRU
• {Koutník14} Koutník J, Greff K, Gomez J F and Schmidhuber J. A Clockwork RNN. Proceedings of ICML 2014, 1863-1871. clockwork RNN
• {LeCun15} Yann LeCun and Yoshua Bengio and Geoffrey Hinton. Deep learning. Nature, 2015, 521:436–444.
• {Xu15} Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Rich Zemel, Yoshua Bengio. Show, Attend and Tell: Neural Image Caption Generation with Visual Attention. Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2048-2057, 2015. attention model
• {Bahdanau15} Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio. Neural Machine Translation by Jointly Learning to Align and Translate. Proc. of ICLR, 2015. attention model
• {Chorowski15} Jan Chorowski, Dzmitry Bahdanau, Dmitriy Serdyuk, Kyunghyun Cho, and Yoshua Bengio. 2015. Attention-based models for speech recognition. Proc. of NIPS, 2015. extend the attention-mechanism with features needed for speech recognition
• {Dong15} Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang. Image Super-Resolution Using Deep Convolutional Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2015, 38(2):295-307. The mapping is represented as a deep convolutional neural network (CNN) that takes the low-resolution image as the input and outputs the high-resolution one
• {Neil16} Neil D, Pfeiffer M and Liu S C. Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences. Proceedings of NIPS, 2016, 3882-3890. Phased LSTM network achieves faster convergence than regular LSTMs on tasks which require learning of long sequences
• {Chen17} Jingyuan Chen, Hanwang Zhang, Xiangnan He, Liqiang Nie, Wei Liu, Tat-Seng Chua. Attentive Collaborative Filtering: Multimedia Recommendation with Item- and Component-Level Attention. Proc. of ACM SIGIR, 2017.
• {Chang17} Shiyu Chang, Yang Zhang, Wei Han, Mo Yu, Xiaoxiao Guo, Wei Tan, Xiaodong Cui, Michael Witbrock, Mark Hasegawa-Johnson, Thomas S. Huang. Dilated Recurrent Neural Networks. Proc. of NIPS, 2017.
• {Li18} Zhuohan Li, Di He, Fei Tian, Wei Chen, Tao Qin, Liwei Wang, and Tie-Yan Liu. Towards Binary-Valued Gates for Robust LSTM Training. Proc. of ICML, 2018.
• {Prokhorenkova18} Liudmila Prokhorenkova, Gleb Gusev, Aleksandr Vorobev, Anna Veronika Dorogush, Andrey Gulin. CatBoost: unbiased boosting with categorical features. Proc. of NIPS, 2018. CatBoost is a fast, scalable, high performance open-source gradient boosting on decision trees library