paper related to NLP applications

source from https://github.com/lipiji/App-DL

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).
• Pathak, Deepak, Pulkit Agrawal, Alexei A. Efros, and Trevor Darrell. “Curiosity-driven Exploration by Self-supervised Prediction.” arXiv preprint arXiv:1705.05363 (2017).

Text Generation

• Rennie, Steven J., Etienne Marcheret, Youssef Mroueh, Jarret Ross, and Vaibhava Goel. “Self-critical sequence training for image captioning.” arXiv preprint arXiv:1612.00563 (2016).
• Lin, Kevin, Dianqi Li, Xiaodong He, Zhengyou Zhang, and Ming-Ting Sun. “Adversarial Ranking for Language Generation.” arXiv preprint arXiv:1705.11001 (2017).
• Zhang, Li, Flood Sung, Feng Liu, Tao Xiang, Shaogang Gong, Yongxin Yang, and Timothy M. Hospedales. “Actor-Critic Sequence Training for Image Captioning.” arXiv preprint arXiv:1706.09601 (2017).
• Wiseman, Sam, Stuart M. Shieber, and Alexander M. Rush. “Challenges in Data-to-Document Generation.” arXiv preprint arXiv:1707.08052 (2017).
• Lebret, Rémi, David Grangier, and Michael Auli. “Neural text generation from structured data with application to the biography domain.” arXiv preprint arXiv:1603.07771 (2016).
• Chisholm, Andrew, Will Radford, and Ben Hachey. “Learning to generate one-sentence biographies from Wikidata.” arXiv preprint arXiv:1702.06235 (2017).
• Sha, Lei, Lili Mou, Tianyu Liu, Pascal Poupart, Sujian Li, Baobao Chang, and Zhifang Sui. “Order-Planning Neural Text Generation From Structured Data.” arXiv preprint arXiv:1709.00155 (2017).
• Jiaxian Guo, Sidi Lu, Han Cai, Weinan Zhang, Yong Yu, Jun Wang. “Long Text Generation via Adversarial Training with Leaked Information.” arXiv preprint arXiv:1709.08624 (2017).
• Guu, Kelvin, Tatsunori B. Hashimoto, Yonatan Oren, and Percy Liang. “Generating Sentences by Editing Prototypes.” arXiv preprint arXiv:1709.08878 (2017).
• Tianyu Liu, Kexiang Wang, Lei Sha, Baobao Chang, Zhifang Sui. “Table-to-text Generation by Structure-aware Seq2seq Learnings.” arXiv preprint arXiv:1711.09724 (2017).
• Kahou, Samira Ebrahimi, Adam Atkinson, Vincent Michalski, Akos Kadar, Adam Trischler, and Yoshua Bengio. “FigureQA: An Annotated Figure Dataset for Visual Reasoning.” arXiv preprint arXiv:1710.07300 (2017).
• Murakami, Soichiro, Akihiko Watanabe, Akira Miyazawa, Keiichi Goshima, Toshihiko Yanase, Hiroya Takamura, and Yusuke Miyao. “Learning to Generate Market Comments from Stock Prices.” In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), vol. 1, pp. 1374-1384. 2017.
• Mueller, Jonas, David Gifford, and Tommi Jaakkola. “Sequence to better sequence: continuous revision of combinatorial structures.” In International Conference on Machine Learning, pp. 2536-2544. 2017.
• Peter J. Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, Noam Shazeer. “Generating Wikipedia by Summarizing Long Sequences.” ICLR 2018.
• Clark, Elizabeth, Anne Spencer Ross, Chenhao Tan, Yangfeng Ji, and Noah A. Smith. “Creative Writing with a Machine in the Loop: Case Studies on Slogans and Stories.” (2018).
• Gehrmann, Sebastian, S. E. A. S. Harvard, Falcon Z. Dai, Henry Elder, and Alexander M. Rush. “End-to-End Content and Plan Selection for Natural Language Generation.”

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)
• Zhang, Jianmin, Jin-ge Yao, and Xiaojun Wan. “Toward constructing sports news from live text commentary.” In Proceedings of 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 Sentence-wise Optimization”. arXiv:1604.01904 (2016)
• Kikuchi, Yuta, Graham Neubig, Ryohei Sasano, Hiroya Takamura, and Manabu Okumura. “Controlling Output Length in Neural Encoder-Decoders.” arXiv preprint arXiv:1609.09552 (2016).
• Qian Chen, Xiaodan Zhu, Zhenhua Ling, Si Wei and Hui Jiang. “Distraction-Based Neural Networks 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.
• Takase, Sho, Jun Suzuki, Naoaki Okazaki, Tsutomu Hirao, and Masaaki Nagata. “Neural headline generation on abstract meaning representation.” EMNLP, pp. 1054-1059. 2016.
• Hongya Song, Zhaochun Ren, Piji Li, Shangsong Liang, Jun Ma, and Maarten de Rijke. Summarizing Answers in Non-Factoid Community Question-Answering. In WSDM 2017: The 10th International Conference on Web Search and Data Mining, 2017.
• Wenyuan Zeng, Wenjie Luo, Sanja Fidler, Raquel Urtasun. “Efficient Summarization with Read-Again and Copy Mechanism.” arXiv preprint arXiv:1611.03382 (2016).
• Piji Li, Zihao Wang, Wai Lam, Zhaochun Ren, Lidong Bing. “Salience Estimation via Variational Auto-Encoders for Multi-Document Summarization”. In AAAI, 2017.
• Ramesh Nallapati, Feifei Zhai, Bowen Zhou. SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents. In AAAI, 2017.
• Ramesh Nallapati, Bowen Zhou, Mingbo Ma. “Classify or Select: Neural Architectures for Extractive Document Summarization.” arXiv preprint arXiv:1611.04244 (2016).
• Suzuki, Jun, and Masaaki Nagata. “Cutting-off Redundant Repeating Generations for Neural Abstractive Summarization.” EACL 2017 (2017): 291.
• Jiwei Tan and Xiaojun Wan. Abstractive Document Summarization with a Graph-Based Attentional Neural Model. ACL, 2017.
• Preksha Nema, Mitesh M. Khapra, Balaraman Ravindran and Anirban Laha. Diversity driven attention model for query-based abstractive summarization. ACL,2017
• Abigail See, Peter J. Liu and Christopher D. Manning. Get To The Point: Summarization with Pointer-Generator Networks. ACL, 2017.
• Qingyu Zhou, Nan Yang, Furu Wei and Ming Zhou. Selective Encoding for Abstractive Sentence Summarization. ACL, 2017
• Maxime Peyrard and Judith Eckle-Kohler. Supervised Learning of Automatic Pyramid for Optimization-Based Multi-Document Summarization. ACL, 2017.
• Shashi Narayan, Nikos Papasarantopoulos, Mirella Lapata, Shay B. Cohen. “Neural Extractive Summarization with Side Information.” arXiv preprint arXiv:1704.04530 (2017).
• Romain Paulus, Caiming Xiong, Richard Socher. “A Deep Reinforced Model for Abstractive Summarization.” (2017).
• Shibhansh Dohare, Harish Karnick. “Text Summarization using Abstract Meaning Representation.” arXiv:1706.01678 (2017).
• Michihiro Yasunaga, Rui Zhang, Kshitijh Meelu, Ayush Pareek, Krishnan Srinivasan, Dragomir Radev. “Graph-based Neural Multi-Document Summarization.” arXiv:1706.06681 (2017).
• Piji Li, Wai Lam, Lidong Bing, and Zihao Wang. Deep Recurrent Generative Decoder for Abstractive Text Summarization. Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP’17). Sep 2017.
• Piji Li, Wai Lam, Lidong Bing, Weiwei Guo, and Hang Li. Cascaded Attention based Unsupervised Information Distillation for Compressive Summarization. Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP’17). Sep 2017.
• Piji Li, Lidong Bing, Wai Lam. Reader-Aware Multi-Document Summarization: An Enhanced Model and The First Dataset. Proceedings of the EMNLP 2017 Workshop on New Frontiers in Summarization (EMNLP-NewSum’17). Sep 2017.
• Tan, Jiwei, Xiaojun Wan, and Jianguo Xiao. “From Neural Sentence Summarization to Headline Generation: A Coarse-to-Fine Approach.” IJCAI 2017.
• Ling, Jeffrey, and Alexander M. Rush. “Coarse-to-Fine Attention Models for Document Summarization.” EMNLP 2017 (2017): 33.
• Ziqiang Cao, Furu Wei, Wenjie Li, Sujian Li. “Faithful to the Original: Fact Aware Neural Abstractive Summarization.” arXiv:1711.04434 (2017).
• Angela Fan, David Grangier, Michael Auli. “Controllable Abstractive Summarization.” arXiv:1711.05217 (2017).
• Narayan, Shashi, Shay B. Cohen, and Mirella Lapata. “Ranking Sentences for Extractive Summarization with Reinforcement Learning.” arXiv preprint arXiv:1802.08636 (2018).  - Asli Celikyilmaz, Antoine Bosselut, Xiaodong He, Yejin Choi. “Deep Communicating Agents for Abstractive Summarization.” NAACL (2018).
• Li, Piji, Lidong Bing, and Wai Lam. “Actor-Critic based Training Framework for Abstractive Summarization.” arXiv preprint arXiv:1803.11070 (2018).

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.
• Piji Li, Zihao Wang, Zhaochun Ren, Lidong Bing, Wai Lam. “Neural Rating Regression with Abstractive Tips Generation for Recommendation.”. In SIGIR, pp xx-xx. 2017.   ### Video Summarization
• Zhou, Kaiyang, and Yu Qiao. “Deep Reinforcement Learning for Unsupervised Video Summarization with Diversity-Representativeness Reward.” arXiv preprint arXiv:1801.00054 (2017).
• Mahasseni, Behrooz, Michael Lam, and Sinisa Todorovic. “Unsupervised video summarization with adversarial lstm networks.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2017.

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.
• Minghao Hu, Yuxing Peng, Xipeng Qiu. “Mnemonic Reader for Machine Comprehension.” arXiv:1705.02798 (2017).
• Wenhui Wang, Nan Yang, Furu Wei, Baobao Chang and Ming Zhou. “R-NET: Machine Reading Comprehension with Self-matching Networks.” ACL (2017).

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

• Bordes, Antoine, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. “Translating embeddings for modeling multi-relational data.” In Advances in Neural Information Processing Systems, pp. 2787-2795. 2013. TransE
• Lin, Yankai, Shiqi Shen, Zhiyuan Liu, Huanbo Luan, and Maosong Sun. “Neural Relation Extraction with Selective Attention over Instances.” ACL (2016)
• TransXXX

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).
• Xiong, Caiming, Stephen Merity, and Richard Socher. “Dynamic memory networks for visual and textual question answering.” arXiv preprint arXiv:1603.01417 (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.
• Bengio, Samy, Oriol Vinyals, Navdeep Jaitly, and Noam Shazeer. “Scheduled sampling for sequence prediction with recurrent neural networks.” In Advances in Neural Information Processing Systems, pp. 1171-1179. 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).
• Zhilin Yang, Ye Yuan, Yuexin Wu, Ruslan Salakhutdinov, William W. Cohen. “Review Networks for Caption Generation.” arXiv preprint arXiv:1605.07912 (2016).
• Xiang Li, Tao Qin, Jian Yang, Tie-Yan Liu. “LightRNN: Memory and Computation-Efficient Recurrent Neural Networks.” arXiv preprint arXiv:1610.09893 (2016).
• Zhaopeng Tu, Yang Liu, Lifeng Shang, Xiaohua Liu, Hang Li. “Neural Machine Translation with Reconstruction.” arXiv preprint arXiv:1611.01874 (2016).
• Yingce Xia, Di He, Tao Qin, Liwei Wang, Nenghai Yu, Tie-Yan Liu, Wei-Ying Ma. “Dual Learning for Machine Translation.” arXiv preprint arXiv:1611.00179 (2016).
• 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).
• Kannan, Anjuli, and Oriol Vinyals. “Adversarial evaluation of dialogue models.” arXiv preprint arXiv:1701.08198 (2017).
• Kawthekar, Prasad, Raunaq Rewari, and Suvrat Bhooshan. “Evaluating Generative Models for Text Generation.”
• Li, Jiwei, Will Monroe, Tianlin Shi, Alan Ritter, and Dan Jurafsky. “Adversarial Learning for Neural Dialogue Generation.” arXiv preprint arXiv:1701.06547 (2017).
• Yang, Zhen, Wei Chen, Feng Wang, and Bo Xu. “Improving Neural Machine Translation with Conditional Sequence Generative Adversarial Nets.” arXiv preprint arXiv:1703.04887 (2017).
• Lijun Wu, Yingce Xia, Li Zhao, Fei Tian, Tao Qin, Jianhuang Lai, Tie-Yan Liu. “Adversarial Neural Machine Translation.” IJCAI (2017).
• Liu, Pengfei, Xipeng Qiu, and Xuanjing Huang. “Adversarial Multi-task Learning for Text Classification.” arXiv preprint arXiv:1704.05742 (2017).
• Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, Yann N. Dauphin. “[Convolutional Sequence to Sequence Learning (https://arxiv.org/abs/1705.03122).” arXiv:1705.03122 (2017).
• Lamb, Alex M., Anirudh Goyal ALIAS PARTH GOYAL, Ying Zhang, Saizheng Zhang, Aaron C. Courville, and Yoshua Bengio. “Professor forcing: A new algorithm for training recurrent networks.” In Advances In Neural Information Processing Systems, pp. 4601-4609. 2016.
• 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).
• Shengjia Zhao, Jiaming Song, Stefano Ermon. “InfoVAE: Information Maximizing Variational Autoencoders.” arXiv:1706.02262 (2017).
• 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).
• Augustus Odena, Christopher Olah, Jonathon Shlens. “Conditional Image Synthesis With Auxiliary Classifier GANs.” arXiv preprint arXiv:1610.09585 (2016).
• Ian Goodfellow. “NIPS Tutorial: GANs”, NIPS, 2016
• Che, Tong, Yanran Li, Ruixiang Zhang, R. Devon Hjelm, Wenjie Li, Yangqiu Song, and Yoshua Bengio. “Maximum-Likelihood Augmented Discrete Generative Adversarial Networks.” arXiv preprint arXiv:1702.07983 (2017).
• Junbo (Jake) Zhao, Yoon Kim, Kelly Zhang, Alexander M. Rush, Yann LeCun. “Adversarially Regularized Autoencoders for Generating Discrete Structures.” arXiv preprint arXiv:1706.04223 (2017).
• Mike Lewis Denis Yarats Yann N. Dauphin Devi Parikh Dhruv Batra . “Deal or No Deal? End-to-End Learning for Negotiation Dialogues.” (2017).
• Mihaela Rosca, Balaji Lakshminarayanan, David Warde-Farley, Shakir Mohamed. “Variational Approaches for Auto-Encoding Generative Adversarial Networks.” arXiv preprint arXiv:1706.04987 (2017).
• Goyal, Prasoon, Zhiting Hu, Xiaodan Liang, Chenyu Wang, and Eric Xing. “Nonparametric Variational Auto-encoders for Hierarchical Representation Learning.” arXiv preprint arXiv:1703.07027 (2017).
• Sabour, Sara, Nicholas Frosst, and Geoffrey Hinton. “Dynamic Routing between Capsules.” (2017).
• Vaswani, Ashish, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. “Attention is all you need.” NIPS. 2017.

Recommendation System

• Salakhutdinov, Ruslan, Andriy Mnih, and Geoffrey Hinton. “Restricted Boltzmann machines for collaborative filtering.” In Proceedings of the 24th international conference on Machine learning, pp. 791-798. ACM, 2007.
• Wang, Hao, Xingjian Shi, and Dit-Yan Yeung. “Relational Stacked Denoising Autoencoder for Tag Recommendation.” In AAAI, pp. 3052-3058. 2015.
• Wang, Hao, Naiyan Wang, and Dit-Yan Yeung. “Collaborative deep learning for recommender systems.” In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1235-1244. ACM, 2015.
• Covington, Paul, Jay Adams, and Emre Sargin. “Deep neural networks for youtube recommendations.” In Proceedings of the 10th ACM Conference on Recommender Systems, pp. 191-198. ACM, 2016.
• Devooght, Robin, and Hugues Bersini. “Collaborative Filtering with Recurrent Neural Networks.” arXiv preprint arXiv:1608.07400 (2016).
• Wang, Hao, S. H. I. Xingjian, and Dit-Yan Yeung. “Collaborative recurrent autoencoder: Recommend while learning to fill in the blanks.” In Advances in Neural Information Processing Systems, pp. 415-423. 2016.
• Tang, Jian, Yifan Yang, Sam Carton, Ming Zhang, and Qiaozhu Mei. “Context-aware Natural Language Generation with Recurrent Neural Networks.” arXiv preprint arXiv:1611.09900 (2016).
• Zhang, Fuzheng, Nicholas Jing Yuan, Defu Lian, Xing Xie, and Wei-Ying Ma. “Collaborative Knowledge Base Embedding for Recommender Systems.” KDD, 2016.
• Dong, Li, Shaohan Huang, Furu Wei, Mirella Lapata, Ming Zhou, and Ke XuΤ. “Learning to Generate Product Reviews from Attributes.” EACL, 2017.
• He, Xiangnan. “Neural Collaborative Filtering.” WWW, 2017
• Wu, Chao-Yuan, Amr Ahmed, Alex Beutel, Alexander J. Smola, and How Jing. “Recurrent Recommender Networks.” Training 10, no. 2: 10-1.2017
• Radford, Alec, Rafal Jozefowicz, and Ilya Sutskever. “Learning to generate reviews and discovering sentiment.” arXiv preprint arXiv:1704.01444 (2017).
• Piji Li, Zihao Wang, Zhaochun Ren, Lidong Bing, Wai Lam. “Neural Rating Regression with Abstractive Tips Generation for Recommendation.”. In SIGIR, pp xx-xx. 2017.

Network Representation Learning

• Must-read papers on network representation learning (NRL)/network embedding (NE)

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)