Get our free extension to see links to code for papers anywhere online!

Chrome logo Add to Chrome

Firefox logo Add to Firefox
Picture for Zhouhan Lin

Zhouhan Lin

Reciprocal Supervised Learning Improves Neural Machine Translation


Dec 05, 2020
Minkai Xu, Mingxuan Wang, Zhouhan Lin, Hao Zhou, Weinan Zhang, Lei Li

Figure 1 for Reciprocal Supervised Learning Improves Neural Machine Translation
Figure 2 for Reciprocal Supervised Learning Improves Neural Machine Translation
Figure 3 for Reciprocal Supervised Learning Improves Neural Machine Translation
Figure 4 for Reciprocal Supervised Learning Improves Neural Machine Translation

  Access Paper or Ask Questions

Exploiting Syntactic Structure for Better Language Modeling: A Syntactic Distance Approach


May 12, 2020
Wenyu Du, Zhouhan Lin, Yikang Shen, Timothy J. O'Donnell, Yoshua Bengio, Yue Zhang

Figure 1 for Exploiting Syntactic Structure for Better Language Modeling: A Syntactic Distance Approach
Figure 2 for Exploiting Syntactic Structure for Better Language Modeling: A Syntactic Distance Approach
Figure 3 for Exploiting Syntactic Structure for Better Language Modeling: A Syntactic Distance Approach
Figure 4 for Exploiting Syntactic Structure for Better Language Modeling: A Syntactic Distance Approach
* ACL20 
  Access Paper or Ask Questions

Ordered Memory


Nov 03, 2019
Yikang Shen, Shawn Tan, Arian Hosseini, Zhouhan Lin, Alessandro Sordoni, Aaron Courville

Figure 1 for Ordered Memory
Figure 2 for Ordered Memory
Figure 3 for Ordered Memory
Figure 4 for Ordered Memory
* Published in NeurIPS 2019 
  Access Paper or Ask Questions

Interactive Language Learning by Question Answering


Aug 28, 2019
Xingdi Yuan, Marc-Alexandre Cote, Jie Fu, Zhouhan Lin, Christopher Pal, Yoshua Bengio, Adam Trischler

Figure 1 for Interactive Language Learning by Question Answering
Figure 2 for Interactive Language Learning by Question Answering
Figure 3 for Interactive Language Learning by Question Answering
Figure 4 for Interactive Language Learning by Question Answering
* EMNLP 2019 
  Access Paper or Ask Questions

Focused Hierarchical RNNs for Conditional Sequence Processing


Jun 12, 2018
Nan Rosemary Ke, Konrad Zolna, Alessandro Sordoni, Zhouhan Lin, Adam Trischler, Yoshua Bengio, Joelle Pineau, Laurent Charlin, Chris Pal

Figure 1 for Focused Hierarchical RNNs for Conditional Sequence Processing
Figure 2 for Focused Hierarchical RNNs for Conditional Sequence Processing
Figure 3 for Focused Hierarchical RNNs for Conditional Sequence Processing
Figure 4 for Focused Hierarchical RNNs for Conditional Sequence Processing
* To appear at ICML 2018 
  Access Paper or Ask Questions

Straight to the Tree: Constituency Parsing with Neural Syntactic Distance


Jun 11, 2018
Yikang Shen, Zhouhan Lin, Athul Paul Jacob, Alessandro Sordoni, Aaron Courville, Yoshua Bengio

Figure 1 for Straight to the Tree: Constituency Parsing with Neural Syntactic Distance
Figure 2 for Straight to the Tree: Constituency Parsing with Neural Syntactic Distance
Figure 3 for Straight to the Tree: Constituency Parsing with Neural Syntactic Distance
Figure 4 for Straight to the Tree: Constituency Parsing with Neural Syntactic Distance
* Published at ACL2018 
  Access Paper or Ask Questions

Neural Language Modeling by Jointly Learning Syntax and Lexicon


Feb 19, 2018
Yikang Shen, Zhouhan Lin, Chin-Wei Huang, Aaron Courville

Figure 1 for Neural Language Modeling by Jointly Learning Syntax and Lexicon
Figure 2 for Neural Language Modeling by Jointly Learning Syntax and Lexicon
Figure 3 for Neural Language Modeling by Jointly Learning Syntax and Lexicon
Figure 4 for Neural Language Modeling by Jointly Learning Syntax and Lexicon
* 16 pages, 5 figures, ICLR 2018 
  Access Paper or Ask Questions

A Deep Reinforcement Learning Chatbot (Short Version)


Jan 20, 2018
Iulian V. Serban, Chinnadhurai Sankar, Mathieu Germain, Saizheng Zhang, Zhouhan Lin, Sandeep Subramanian, Taesup Kim, Michael Pieper, Sarath Chandar, Nan Rosemary Ke, Sai Rajeswar, Alexandre de Brebisson, Jose M. R. Sotelo, Dendi Suhubdy, Vincent Michalski, Alexandre Nguyen, Joelle Pineau, Yoshua Bengio

Figure 1 for A Deep Reinforcement Learning Chatbot (Short Version)
Figure 2 for A Deep Reinforcement Learning Chatbot (Short Version)
Figure 3 for A Deep Reinforcement Learning Chatbot (Short Version)
* 9 pages, 1 figure, 2 tables; presented at NIPS 2017, Conversational AI: "Today's Practice and Tomorrow's Potential" Workshop 
  Access Paper or Ask Questions

A Deep Reinforcement Learning Chatbot


Nov 05, 2017
Iulian V. Serban, Chinnadhurai Sankar, Mathieu Germain, Saizheng Zhang, Zhouhan Lin, Sandeep Subramanian, Taesup Kim, Michael Pieper, Sarath Chandar, Nan Rosemary Ke, Sai Rajeshwar, Alexandre de Brebisson, Jose M. R. Sotelo, Dendi Suhubdy, Vincent Michalski, Alexandre Nguyen, Joelle Pineau, Yoshua Bengio

Figure 1 for A Deep Reinforcement Learning Chatbot
Figure 2 for A Deep Reinforcement Learning Chatbot
Figure 3 for A Deep Reinforcement Learning Chatbot
Figure 4 for A Deep Reinforcement Learning Chatbot
* 40 pages, 9 figures, 11 tables 
  Access Paper or Ask Questions

A Structured Self-attentive Sentence Embedding


Mar 09, 2017
Zhouhan Lin, Minwei Feng, Cicero Nogueira dos Santos, Mo Yu, Bing Xiang, Bowen Zhou, Yoshua Bengio

Figure 1 for A Structured Self-attentive Sentence Embedding
Figure 2 for A Structured Self-attentive Sentence Embedding
Figure 3 for A Structured Self-attentive Sentence Embedding
Figure 4 for A Structured Self-attentive Sentence Embedding
* 15 pages with appendix, 7 figures, 4 tables. Conference paper in 5th International Conference on Learning Representations (ICLR 2017) 
  Access Paper or Ask Questions

Recurrent Neural Networks With Limited Numerical Precision


Feb 26, 2017
Joachim Ott, Zhouhan Lin, Ying Zhang, Shih-Chii Liu, Yoshua Bengio

Figure 1 for Recurrent Neural Networks With Limited Numerical Precision
Figure 2 for Recurrent Neural Networks With Limited Numerical Precision
Figure 3 for Recurrent Neural Networks With Limited Numerical Precision
* NIPS 2016 EMDNN Workshop paper, condensed version of arXiv:1608.06902 
  Access Paper or Ask Questions

Architectural Complexity Measures of Recurrent Neural Networks


Nov 12, 2016
Saizheng Zhang, Yuhuai Wu, Tong Che, Zhouhan Lin, Roland Memisevic, Ruslan Salakhutdinov, Yoshua Bengio

Figure 1 for Architectural Complexity Measures of Recurrent Neural Networks
Figure 2 for Architectural Complexity Measures of Recurrent Neural Networks
Figure 3 for Architectural Complexity Measures of Recurrent Neural Networks
Figure 4 for Architectural Complexity Measures of Recurrent Neural Networks
* 17 pages, 8 figures; To appear in NIPS2016 
  Access Paper or Ask Questions

Towards Biologically Plausible Deep Learning


Aug 09, 2016
Yoshua Bengio, Dong-Hyun Lee, Jorg Bornschein, Thomas Mesnard, Zhouhan Lin

Figure 1 for Towards Biologically Plausible Deep Learning
Figure 2 for Towards Biologically Plausible Deep Learning
Figure 3 for Towards Biologically Plausible Deep Learning
Figure 4 for Towards Biologically Plausible Deep Learning

  Access Paper or Ask Questions

Theano: A Python framework for fast computation of mathematical expressions


May 09, 2016
The Theano Development Team, Rami Al-Rfou, Guillaume Alain, Amjad Almahairi, Christof Angermueller, Dzmitry Bahdanau, Nicolas Ballas, Frédéric Bastien, Justin Bayer, Anatoly Belikov, Alexander Belopolsky, Yoshua Bengio, Arnaud Bergeron, James Bergstra, Valentin Bisson, Josh Bleecher Snyder, Nicolas Bouchard, Nicolas Boulanger-Lewandowski, Xavier Bouthillier, Alexandre de Brébisson, Olivier Breuleux, Pierre-Luc Carrier, Kyunghyun Cho, Jan Chorowski, Paul Christiano, Tim Cooijmans, Marc-Alexandre Côté, Myriam Côté, Aaron Courville, Yann N. Dauphin, Olivier Delalleau, Julien Demouth, Guillaume Desjardins, Sander Dieleman, Laurent Dinh, Mélanie Ducoffe, Vincent Dumoulin, Samira Ebrahimi Kahou, Dumitru Erhan, Ziye Fan, Orhan Firat, Mathieu Germain, Xavier Glorot, Ian Goodfellow, Matt Graham, Caglar Gulcehre, Philippe Hamel, Iban Harlouchet, Jean-Philippe Heng, Balázs Hidasi, Sina Honari, Arjun Jain, Sébastien Jean, Kai Jia, Mikhail Korobov, Vivek Kulkarni, Alex Lamb, Pascal Lamblin, Eric Larsen, César Laurent, Sean Lee, Simon Lefrancois, Simon Lemieux, Nicholas Léonard, Zhouhan Lin, Jesse A. Livezey, Cory Lorenz, Jeremiah Lowin, Qianli Ma, Pierre-Antoine Manzagol, Olivier Mastropietro, Robert T. McGibbon, Roland Memisevic, Bart van Merriënboer, Vincent Michalski, Mehdi Mirza, Alberto Orlandi, Christopher Pal, Razvan Pascanu, Mohammad Pezeshki, Colin Raffel, Daniel Renshaw, Matthew Rocklin, Adriana Romero, Markus Roth, Peter Sadowski, John Salvatier, François Savard, Jan Schlüter, John Schulman, Gabriel Schwartz, Iulian Vlad Serban, Dmitriy Serdyuk, Samira Shabanian, Étienne Simon, Sigurd Spieckermann, S. Ramana Subramanyam, Jakub Sygnowski, Jérémie Tanguay, Gijs van Tulder, Joseph Turian, Sebastian Urban, Pascal Vincent, Francesco Visin, Harm de Vries, David Warde-Farley, Dustin J. Webb, Matthew Willson, Kelvin Xu, Lijun Xue, Li Yao, Saizheng Zhang, Ying Zhang

Figure 1 for Theano: A Python framework for fast computation of mathematical expressions
Figure 2 for Theano: A Python framework for fast computation of mathematical expressions
Figure 3 for Theano: A Python framework for fast computation of mathematical expressions
Figure 4 for Theano: A Python framework for fast computation of mathematical expressions
* 19 pages, 5 figures 
  Access Paper or Ask Questions

Neural Networks with Few Multiplications


Feb 26, 2016
Zhouhan Lin, Matthieu Courbariaux, Roland Memisevic, Yoshua Bengio

Figure 1 for Neural Networks with Few Multiplications
Figure 2 for Neural Networks with Few Multiplications
Figure 3 for Neural Networks with Few Multiplications
Figure 4 for Neural Networks with Few Multiplications
* Published as a conference paper at ICLR 2016. 9 pages, 3 figures 
  Access Paper or Ask Questions

Spectral-Spatial Classification of Hyperspectral Image Using Autoencoders


Nov 09, 2015
Zhouhan Lin, Yushi Chen, Xing Zhao, Gang Wang

Figure 1 for Spectral-Spatial Classification of Hyperspectral Image Using Autoencoders
Figure 2 for Spectral-Spatial Classification of Hyperspectral Image Using Autoencoders
Figure 3 for Spectral-Spatial Classification of Hyperspectral Image Using Autoencoders
Figure 4 for Spectral-Spatial Classification of Hyperspectral Image Using Autoencoders
* Accepted as a conference paper at ICICS 2013, an updated version. Codes published. 9 pages, 6 figures 
  Access Paper or Ask Questions

How far can we go without convolution: Improving fully-connected networks


Nov 09, 2015
Zhouhan Lin, Roland Memisevic, Kishore Konda

Figure 1 for How far can we go without convolution: Improving fully-connected networks
Figure 2 for How far can we go without convolution: Improving fully-connected networks
Figure 3 for How far can we go without convolution: Improving fully-connected networks
Figure 4 for How far can we go without convolution: Improving fully-connected networks
* 10 pages, 11 figures, submitted for ICLR 2016 
  Access Paper or Ask Questions