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https://github.com/kumarkrishna/paper-spray
List of interesting papers to read
https://github.com/kumarkrishna/paper-spray
Last synced: 2 months ago
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List of interesting papers to read
- Host: GitHub
- URL: https://github.com/kumarkrishna/paper-spray
- Owner: kumarkrishna
- Created: 2016-05-17T15:24:54.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2017-09-04T19:42:57.000Z (over 7 years ago)
- Last Synced: 2024-08-03T22:02:55.459Z (6 months ago)
- Language: Python
- Size: 270 KB
- Stars: 64
- Watchers: 9
- Forks: 21
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-papers - kumarkrishna/paper-spray
README
# Paper-Spray
This is a list of interesting research papers started by
[Kumar](https://github.com/kumarkrishna) and [Biswa](https://github.com/biswajitsc) (currently being maintained only by Kumar),
mainly in Machine Learning, but definitely not limited to it.
This is mainly an initiative to inculcate a reading habit among ourselves.
Suggested reads are always welcome!
We would try submit only links which are freely available, but we may also add
few links which can be accessed freely only from an university network.
__We have created a webpage for [Paper-Spray](https://biswajitsc.github.io/paper-spray.html)
containing a searchable list of the papers in the json file.__
Entry format:
> * Paper Title
> ```Date Added, Keywords```
> ```Author, Conference, Year```
Abbreviations:
* AI: Artificial Intelligence
* CV : Computer Vision
* DL: Deep Learning
* ML : Machine Learning
* NLP : Natural Language Processing
* RL : Reinforcement Learning
## How it works?
The papers are added to ```paper-list.json```. They can either be added
manually or by using the ```add_papers.py``` script. Thereafter the README is
generated by using the ```create_readme.py``` script. This script appends the paper
names present in the json file to the contents of ```readme.template```,
to generate ```README.md```.
Some scripts such as ```add_papers.sh``` and ```add_papers_minimal.sh``` have
been created for convenience.
The scripts give a warning when adding duplicate papers. In that case,
enter 'n' when asked to abort adding the paper.
The webpage for paper-spray reads the json file and creates a table using js libraries.
There is no need of generating static html pages for any change in the json file.
## Papers
* Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation
```17/08/2017, RL, K-FAC```
```Yuhuai Wu, Elman Mansimov, Shun Liao, Roger Grosse, Jimmy Ba, arXiv``` \[Review\]
* Function Optimization Using Connectionist Reinforcement Learning Algorithms
```16/08/2017, RL```
```Ronald J. Williams, Jing Peng, Connection Science```
* How to Escape Saddle Points Efficiently
```16/08/2017, ML, Non-Convex```
```Chi Jin, Rong Ge, Praneeth Netrapalli, Sham M. Kakade, Michael I. Jordan, ICML 2017``` \[Review\]
* Emergence of Locomotion Behaviours in Rich Environments
```16/07/2017, RL, Robotics, PPO```
```Nicolas Heess, Dhruva TB, Srinivasan Sriram, Jay Lemmon, Josh Merel, Greg Wayne, Yuval Tassa, Tom Erez, Ziyu Wang, S. M. Ali Eslami, Martin Riedmiller, David Silver, arXiv```
* Continuous control with deep reinforcement learning
```16/07/2017, RL, DDPG```
```Timothy P. Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, Daan Wierstra, ICLR 2016```
* Trust Region Policy Optimization
```16/07/2017, RL, Robotics```
```John Schulman, Sergey Levine, Philipp Moritz, Michael Jorda, Pieter Abbeel, ICML, 2015```
* The Reversible Residual Network: Backpropagation Without Storing Activations
```14/06/2017, CV, RevNets```
```Aidan N. Gomez, Mengye Ren, Raquel Urtasun, Roger B. Grosse, arXiv```
* Preconditioning Kernel Matrices
```15/12/2016, kernel methods```
```Kurt Cutajar, Michael Osborne, John Cunningham, Maurizio Filippone, ICML 2016```
* Image-to-Image Translation with Conditional Adversarial Networks
```15/12/2016, CV, DL, GAN```
```Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, Alexei A.Efros, arxiv```
* Continous Control with Deep Reinforcement Learning
```15/12/2016, DL, RL, DDPG```
```Timothy P. Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, Daan Wierstra, ICLR 2016```
* Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space
```04/12/2016, generative model, latent space```
```Anh Nguyen, Jason Yosinski, Yoshua Bengio, Alexey Dosovitskiy, Jeff Clune, arXiv```
* Semantic Facial Expression Editing using Autoencoded Flow
```04/12/2016, autoencoder, latent space, image manipulation```
```Raymond Yeh, Ziwei Liu, Dan B Goldman, Aseem Agarwala, arXiv```
* Full-Capacity Unitary Recurrent Neural Networks
```02/11/2016, DL```
```Scott Wisdom, Thomas Powers, John R. Hershey, Jonathan Le Roux, Les Atlas, NIPS 2016```
* Conditional Image Synthesis With Auxiliary Classifier GANs
```02/11/2016, CV, DL, GAN```
```Augustus Odena, Christopher Olan, Jonatho Shlens, arxiv```
* Stochastic Variational Deep Kernel Learning
```02/11/2016, DL, ML```
```Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov, Eric P. Xing, NIPS 2016```
* Multi-Scale Context Aggregation by Dilated Convolutions
```01/11/2016, CV, DL```
```Fisher Yu, Vladlen Klotun, ICLR 2016```
* Neural Machine Translation in Linear Time
```01/11/2016, NMT, DL, dilated-convolutions```
```Nal Kalchbrenner, Lasse Espeholt, Karen Simonyan, Aaron van den Oord, Alex Graves, Koray Kavukcuoglu, arxiv```
* Recurrent Highway Networks
```01/11/2016, DL, RNN```
```Julian Georg Zilly, Rupesh Kumar Srivastava, Jan Koutnik, Jurgen Schmidhuber, arxiv```
* Recurrent Switching Linear Dynamical Systems
```31/10/2016, ML```
```Scott Linderman, Andrew Miller, Ryan Adams, David Blei, Liam Paninski, Matthew Johnson, arxiv```
* Operator Variational Inference
```31/10/2016, ML, variational```
```Rajesh Ranganath, Jaan ALtosaar, Dustin Tran, David M. Blei, arxiv```
* Professor Forcing: A New Algorithm for Training Recurrent Networks
```31/10/2016, DL, RNN```
```Alex Lamb, Anirudh Goyal, Ying Zhang, Saizheng Zhang, Aaron Courville, Yoshua Bengio, NIPS 2016```
* Pointer Sentinel Mixture Models
```30/10/2016, DL, NLP```
```Stephen Merity, Caiming Xiong, James Bradbury, Richard Socher, rxiv```
* Can Active Memory Replace Attention?
```28/10/2016, DL```
```Lukasz Kaiser, Samy Bengio, NIPS 2016```
* Analysis of Thompson Sampling for the Multi-armed Bandit Problem
```25/10/2016, Sampling, Bandits```
```Shipra Agrawal, Navin Goyal, JMLR 2012```
* Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models
```25/10/2016, DL, RL```
```Bradly Stadie, Sergey Levine, Pieter Abbeel, arxiv```
* Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks
```22/10/2016, DL, optimization, bayesian```
```José Miguel Hernández-Lobato, Ryan P. Adams, JMLR```
* Towards Deep Symbolic Reinforcement Learning
```21/10/2016, DL, RL```
```Marta Garnelo, Kai Arulkumaran, Murray Shanahan, arxiv```
* Layer Normalization
```21/10/2016, DL, optimization```
```Jimmy Lei Ba, Jamie Ryan Kiros, Geoffrey E. Hinton, arXiv```
* A Theory of Generative ConvNet
```21/10/2016, ML, statistics, generative, cnn```
```Jianwen Xie, Yang Lu, Song-Chun Zhu, Ying Nian Wu, ICML 2016```
* Modular Multitask Reinforcement Learning with Policy Sketches
```06/10/2016, RL, policy sketch```
```Jacob Andreas, Dan Klein, Sergey Levine, ICML 2017```
* A Tutorial on Energy-Based Learning
```27/09/2016, ML, energy models```
```Yann LeCun, Sumit Chopra, Raia Hadsell, Marc’Aurelio Ranzato, and Fu Jie Huang, ```
* SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
```21/09/2016, GAN, sequence generation, policy gradient```
```Lantao Yu, Weinan Zhang, Jun Wang, Yong Yu, arXiv```
* Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
```16/09/2016, CV, GAN, super resolution```
```Christian Ledig, Lucas Theis, Ferenc Huszar, Jose Caballero, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, Wenzhe Shi, arXiv```
* Energy-based Generative Adversarial Network
```16/09/2016, GAN, DL, generative model, energy function```
```Junbo Zhao, Michael Mathieu, Yann LeCun, arXiv```
* Generating Videos with Scene Dynamics
```15/09/2016, CV, DL, GAN```
```Carl Vondrick, Hamed Pirsiavash, Antonio Torralba, NIPS 2016```
* Generative Visual Manipulation on the Natural Image Manifold
```15/09/2016, CV, DL, GAN```
```Jun-Yan Zhu, Philipp Krähenbühl, Eli Shechtman and Alexei A. Efros, ECCV 2016```
* Why does deep and cheap learning work so well?
```10/09/2016, DL, ML, physics```
```Henry W. Lin, Max Tegmark, arXiv```
* Reward Augmented Maximum Likelihood for Neural Structured Prediction
```01/09/2016, DL, RL, MLE```
```Mohammad Norouzi, Samy Bengio, Zhifeng Chen, Navdeep Jaitly, Mike Schuster, Yonghui Wu, Dale Schuurmans, NIPS 2016```
* Densely Connected Convolutional Networks
```28/08/2016, DL, CNN```
```Gao Huang, Zhuang Liu, Kilian Q. Weinberger, arXiv```
* Mollifying Networks
```18/08/2016, ML, optimization```
```Caglar Gulcehre, Marcin Moczulski, Francesco Visin, Yoshua Bengio, arXiv```
* Optimization Methods for Large-Scale Machine Learning
```18/08/2016, ML, optimization```
```Léon Bottou, Frank E. Curtis, Jorge Nocedal, arXiv```
* Deep FisherNet for Object Classification
```02/08/2016, CV, DL, object classification```
```Peng Tang, Xinggang Wang, Baoguang Shi, Xiang Bai, Wenyu Liu, Zhuowen Tu, arXiv```
* Attention-over-Attention Neural Networks for Reading Comprehension
```26/07/2016, DL, NLP, Attention memory```
```Yiming Cui, Zhipeng Chen, Si Wei, arXiv```
* BinaryConnect : Training Deep Neural Networks with binary weights during propagations
```21/07/2016, DL, binary-connect```
```Matthieu Courbariaux, Yoshua Bengio, Jean-Pierre David, NIPS 2015```
* Stochastic backpropagation and approximate inference in deep generative models
```20/07/2016, generative-models```
```Danilo Jimenez Rezende, Shakir Mohamed, Daan Wierstra, ICML 2014```
* Markov Chain Monte Carlo and Variational Inference: Bridging the Gap
```20/07/2016, MCMC, VAE```
```Tim Salimans, Diederik P. Kingma, Max Welling, ICML 2015```
* Efficient approaches for escaping higher order saddle points in non-convex optimization
```19/07/2016, ML, non-convex-optimization```
```Anima Anandkumar, Rong Ge, COLT 2016```
* Gated-Attention Readers for Text Comprehension
```19/07/2016, DL, NLP```
```Bhuwan Dhingra, Hanxiao Liu, William W. Cohen, Ruslan Salakhutdinov, arXiv```
* Tensor decompositions for learning latent variable models
```19/07/2016, ML, TF```
```Anima Anandkumar, Rong Ge, Daniel Hsu, Sham M. Kakade, Matus Telgarsky, JMLR 2014```
* Pixel Recurrent Neural Networks
```19/07/2016, DL, RNN```
```Aaron van den Oord, Nal Kalchbrenner, Koray Kavukcuoglu, ICML 2016```
* NICE: Non-linear Independent Components Estimation
```18/07/2016, DL```
```Laurent Dinh, David Krueger, Yoshua Bengio, ICLR 2015```
* Higher Order Statistical Decorrelation without Information Loss
```18/07/2016, DL, IT```
```Gustavo Deco, Wilfried Brauer, NIPS 1995```
* Conditional Generative Aversarial Nets
```14/07/2016, GAN, DL```
```Mehdi Mirza, Simon Osindero, NIPS DL Workshop, 2014```
* Neural Generative Question Answering
```14/07/2016, DL, NLP, QA```
```Jun Yin, Xin Jiang, Zhengdong Lu, Lifeng Shang, Hang Li, Xiaoming Li, ICLR 2016```
* A Decomposable Attention Model for Natural Language Inference
```09/07/2016, DL, NLP```
```Ankur P. Parikh, Oscar Tackstrom, Dipanjan Das, Jakob Uszkoreit, arXiv```
* Sequence Level Training with Recurrent Neural Networks
```06/07/2016, DL, RNN```
```Marc'Aurelio Ranzato, Sumit Chopra, Michael Auli, Wojciech Zaremba, ICLR 2016```
* Memorability of Image Regions
```04/07/2016, CV, DL```
```Aditya Khosla, Jianxiong Xiao, Antonio Torralba, Aude Oliva, NIPS 2012```
* Character-Aware Neural Language Models
```04/07/2016, DL, NLP```
```Yoon Kim, Yacine Jernite, David Sontag, Alexander M. Rush, AAAI 2016```
* Learning Language Games through Interaction
```03/07/2016, DL, NLP```
```Sida Wang, Percy Liang, Chris Manning, ACL 2016```
* Object Detectors emerge in Deep Scene CNNs
```02/07/2016, CV, DL```
```Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, Antonio Torralba, ICLR 2015```
* An Infinite Restricted Boltzmann Machine
```01/07/2016, ML, RBM```
```Marc-Alexandre Cote, Hugo Larochelle, Neural Computation```
* Learning to See by Moving
```30/06/2016, CV, DL```
```Pulkit Agrawal, Joao Carreira, Jitendra Malik, ICCV 2015```
* Distinguishing cause from effect using observational data: methods and benchmarks
```29/06/2016, ML, cause-inference```
```Joris M. Mooij, Jonas Peters, Dominik Janzing, Jakob Zscheischler, Bernhard Scholkopf, JMLR 2016```
* Neural Variational Inference for Text Processing
```29/06/2016, DL, NLP```
```Yishu Miao, Lei Yu, Phil Blunsom, arXiv```
* Learning to Transduce with Unbounded Memory
```27/06/2016, DL, NTM, neural data structures```
```Edward Grefenstette, Karl Moritz Hermann, Mustafa Suleyman, Phil Blunsom, NIPS 2015```
* Mean Shift, Mode Seeking, and Clustering
```26/06/2016, ML, Clustering```
```Yizong Cheng, IEEE, 1995```
* Adaptive Online Gradient Descent
```25/06/2016, optimization, gradient descent```
```Peter L. Bartlett, Elad Hazan, Alexander Rakhlin, NIPS 2007```
* Visual Genome
```24/06/2016, vision, nlp multimodal dataset```
```Ranjay Krishna et. al., Dataset```
* Learning Visual Predictive Models of Physics for Playing Billiards
```23/06/2016, CV, DL```
```Katerina Fragkiadaki, Pulkit Agrawal, Sergey Levine, Jitendra Malik, ICLR 2016```
* Tutorial on Variational Autoencoders
```22/06/2016, DL, VAE```
```Carl Doersch, arXiv```
* Towards Conceptual Compression
```22/06/2016, DL```
```Karol Gregor, Frederic Besse, Danilo Jimenez Rezende, Ivo Danihelka, Daan Wierstra, arXiv```
* Dynamic Memory Networks for Visual and Textual Question Answering
```22/06/2016, CV, DL, NLP, MemNets```
```Caiming Xiong, Stephen Merity, Richard Socher, ICML 2016```
* Delving Deeper into Convolutional Networks for Learning Video Representations
```22/06/2016, CV, DL , videos```
```Nicolas Ballas, Li Yao, Chris Pal, Aaron Courville, ICLR 2016```
* Describing Videos by Exploiting Temporal Structure
```22/06/2016, CV, DL, video```
```Li Yao, Atousa Torabi, Kyunghyun Cho, Nicolas Ballas, Christopher Pal, Hugo Larochelle, Aaron Courville, ICCV 2015```
* Generative Adversarial Imitation Learning
```21/06/2016, DL, generative```
```Jonathan Ho, Stefano Ermon, arXiv```
* f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization
```21/06/2016, DL, GAN, f-GAN```
```Sebastian Nowozin, Botond Cseke, Ryota Tomioka, arXiv```
* Variational Inference with Normalizing Flows
```21/06/2016, DL, VAE, inference```
```Danilo Jimenez Rezende, Shakir Mohamed, ICML 2015```
* A Recurrent Latent Variable Model for Sequential Data
```20/06/2016, DL, VRNN```
```Junyoung Chung, Kyle Kastner, Laurent Dinh, Kratarth Goel, Aaron Courville, Yoshua Bengio, NIPS 2015```
* Semi-Supervised Learning with Deep Generative Models
```20/06/2016, DL, generative```
```Diederik P. Kingma, Danilo J. Rezende, Shakir Mohamed, Max Welling, NIPS 2014```
* Human-level control through deep reinforcement learning
```19/06/2016, RL, AI, DL```
```Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Nature```
* Variational Dropout and the Local Reparameterization Trick
```19/06/2016, DL, dropout```
```Diederik P. Kingma, Tim Salimans, Max Welling, NIPS 2015```
* Ask Your Neurons: A Neural-based Approach to Answering Questions about Images
```19/06/2016, CV, DL, NLP```
```Mateusz Malinowski, Marcus Rohrbach, Mario Fritz, ICCV 2015```
* Generating Images from Captions with Attention
```19/06/2016, CV, DL```
```Elman Mansimov, Emilio Parisotto, Jimmy Lei Ba, Ruslan Salakhutdinov, ICLR 2016```
* LSTM: A Search Space Odyssey
```19/06/2016, DL, NLP```
```Klaus Greff, Rupesh Kumar Srivastava, Jan Koutnik, Bas R. Steunebrink, Jurgen Schmidhuber, arXiv```
* InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
```18/06/2016, DL, InfoGAN```
```Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, Pieter Abbeel, arXiv```
* Improved Techniques for Training GANs
```18/06/2016, DL, GAN```
```Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, Xi Chen, arXiv```
* Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units
```17/06/2016, DL, CV```
```Wenling Shang, Kihyuk Sohn, Diogo Almeida, and Honglak Lee, ICML 2016```
* Fast dropout training
```17/06/2016, DL, dropout```
```Sida I. Wang, Christopher D. Manning, ICML 2013```
* Stating the Obvious: Extracting Visual Common Sense Knowledge
```15/06/2016, DL, NLP```
```Mark Yatskar, Vicente Ordonez, Ali Farhadi, NAACL 2016```
* Learning to Communicate with Deep Multi-Agent Reinforcement Learning
```15/06/2016, DL, RL```
```Jakob N. Foerster, Yannis M. Assael, Nando de Freitas, Shimon Whiteson, arXiv```
* Safely Interruptible Agents
```15/06/2016, AI, RL, safety```
```Laurent Orseau, Stuart Armstrong, UAI 2016```
* Deep Spatial Autoencoders for Visuomotor Learning
```15/06/2016, CV, DL, RL, robotics```
```Chelsea Finn, Xin Yu Tan, Yan Duan, Trevor Darrell, Sergey Levine, Pieter Abbeeel, ICRA 2016```
* Multi-Bias Non-linear Activation in Deep Neural Networks
```15/06/2016, ML, DL, activation function```
```Hongyang Li, Wanli Ouyang, Xiaogang Wang, ICML 2016```
* Learning Simple Algorithms from Examples
```15/06/2016, ML, DL, AI```
```Wojciech Zaremba, Tomas Mikolov, Armand Joulin, Rob Fergus, ICML 2016```
* Extracting and Composing Robust Features with Denoising Autoencoders
```15/06/2016, DL, DAE```
```Pascal Vincent, Hugo Larochelle, Yoshua Bengio, Pierre-Antoine Manzagol, ICML 2008```
* Sentence Similarity Learning by Lexical Decomposition and Composition
```14/06/2016, DL, NLP```
```Zhiguo Wang, Haitao Mi, Abraham Ittycheriah, arXiv```
* Learning visual groups from co-occurrences in space and time
```14/06/2016, CV, DL```
```Phillip Isola, Daniel Zoran, Dilip Krishnan, Edward H. Adelson, ICLR 2016```
* Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning
```14/06/2016, CV, DL```
```William Lotter, Gabriel Kreiman, David Cox, arxiv```
* Matching Networks for One Shot Learning
```14/06/2016, DL, one-shot```
```Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Koray Kavukcuoglu, Daan Wierstra, arxiv```
* Deep Reinforcement Learning in Large Discrete Action Spaces
```14/06/2016, DL, RL```
```Gabriel Dulac-Arnold, Richard Evans, Hado van Hasselt, Peter Sunehag, Timothy Lillicrap, Jonathan Hunt, Timothy Mann, Theophane Weber, Thomas Degris, Ben Coppin, arxiv```
* Composing graphical models with neural networks for structured representations and fast inference
```14/06/2016, DL, graphical-models```
```Matthew J. Johnson, David Duvenaud, Alexander B. Wiltschko, Sandeep R. Datta, Ryan P. Adams, arXiv```
* Skip-Thought Vectors
```13/06/2016, DL, NLP```
```Ryan Kiros, Yukun Zhu, Ruslan Salakhutdinov, Richard Zemel, Antonio Torralba, Raquel Urtasun, Sanja Fidler, NIPS 2015```
* Visually Indicated Sounds
```12/06/2016, CV, DL```
```Andrew Owens, Philip Isola, Josh McDermott, Antonio Torralba, Edward Adelson, William Freeman, CVPR 2016```
* DRAW: A Recurrent Neural Network for Image Generation
```11/06/2016, CV, DL, DRAW```
```Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra, JMLR 2015```
* Dynamic Capacity Networks
```10/06/2016, DL```
```Amjad Almahairi, Nicolas Ballas, Tim Cooijmans, Yin Zheng, Hugo Larochelle, Aaron Courville, JMLR, 2016```
* Denoising Autoencoder with Modulated Lateral Connections learns Invariant Representations of Natural Images
```10/06/2016, CV, DL, ladder-networks```
```Antii Rasmus, Tapani Raiko, Harri Valpola, ICLR 2015```
* Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
```10/06/2016, CV, DL, DCGAN```
```Alec Radford, Luke Metz, Soumith Chintala, ICLR 2016```
* Improving sentence compression by learning to predict gaze
```09/06/2016, DL, NLP```
```Sigrid Klerke, Yoav Goldberg, Anders Sogaard, NAACL 2016, Best Short Paper```
* Match-SRNN: Modeling the Recursive Matching Structure with Spatial RNN
```08/06/2016, DL, NLP```
```Shengxian Wan, Yanyan Lan, Jun Xu, Jiafeng Guo, IJCAI 2016```
* Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks
```08/06/2016, DL, CV, GAN, LAPGAN```
```Emily Denton, Soumith Chintala, Arthur Szlam, Rob Fergus, NIPS 2015```
* Neural Module Networks
```08/06/2016, DL, CV, visual QA```
```Jacob Andreas, Marcus Rohrbach, Trevor Darrell, Dan Klein, arXiv```
* A Neural Probabilistic Language Model
```07/06/2016, DL, NLP```
```Yoshua Bengio, Rejean Ducharme, Pascal Vincent, Christian Jauvin, JMLR 2003```
* Adversarially Learned Inference
```07/06/2016, ML, DL, inference, generative model```
```Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Alex Lamb, Martin Arjovsky, Olivier Mastropietro, Aaron Courville, Subt. NIPS 2016```
* Learning to Optimize
```06/06/2016, DL, optimization```
```Ke Li, Jitendar Malik, arxiv```
* Auto-Encoding Variational Bayes
```06/06/2016, VAE```
```Diederik P Kingma, Max Welling, ICLR 2014```
* Language Understanding for Text-based Games Using Deep Reinforcement Learning
```06/06/2016, DL, NLP, RL```
```Karthik Narasimhan, Tejas Kulkarni, Regina Barzilay, EMNLP 2015```
* Retrofitting Word Vectors to Semantic Lexicons
```06/06/2016, NLP, word vectors```
```Manaal Faruqui, Jesse Dodge, Sujay K. Jauhar, Chris Dyer, Eduard Hovy, Noah A. Smith, NAACL 2015```
* Noise-Contrastive Estimation of Unnormalized Statistical Models, with Applications to Natural Image Statistics
```06/06/2016, ML, non-parametric estimation```
```Michael U. Gutmann, Aapo Hyvarinen, JMLR 2012```
* Training Products of Experts by Minimizing Contrastive Divergence
```06/06/2016, ML, contrastive divergence```
```Geoffrey E. Hinton, Neural Computation 2002```
* Neural Word Embedding as Implicit Matrix Factorization
```06/06/2016, DL, NLP, word vectors```
```Omer Levy, Yoav Goldberg, NIPS 2014```
* Context Encoders: Feature Learning by Inpainting
```05/06/2016, CV, DL, context-encoder```
```Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, Alexei AEfros, CVPR 2016```
* Long Short-term Memory
```04/06/2016, DL, RNN, LSTM```
```Sepp Hochreiter, Jurgen Schmidhuber, Neural Computation, 1997```
* Maxout Networks
```03/06/2016, DL, dropout, maxout```
```Ian Goodfellow, David Farley, Mehdi Mirza, Aaron Courville, Yoshua Bengio, JMLR 2013```
* A Clockwork RNN
```02/06/2016, DL, RNN, clock-work```
```Jan Koutnik, Klaus Greff, Faustino Gomez, Jurgen Schmidhuber, JMLR 2014```
* Long Short-Term Memory-Networks for Machine Reading
```02/06/2016, DL, NLP, machine understanding```
```Jianpeng Cheng, Li Dong, Mirella Lapata, ```
* Effective Approaches to Attention-based Neural Machine Translation
```02/06/2016, DL, NLP, neural machine translation```
```Minh-Thang Luong, Hieu Pham, Christopher D. Manning, EMNLP 2015```
* A Neural Attention Model for Sentence Summarization
```02/06/2016, DL, NLP, summarization```
```Alexander M. Rush, Sumit Chopra, Jason Weston, EMNLP 2015```
* To See or not to See : The need for attention to perrceive changes in scenes
```01/06/2016, attention, vision```
```Ronald Rensink, Kevin O'Regan, James Clark, Psychological Science, 1997```
* Teaching Machines to Read and Comprehend
```01/06/2016, DL, NLP, attention```
```Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, Phil Blunsom, NIPS 2015```
* Recurrent Models of Visual Attention
```01/06/2016, CV, DL, RL, attention```
```Volodymyr Mnih, Nicolas Heess, Alex Graves, Koray Kavukcuoglu, NIPS 2014```
* Learning to compose neural networks for question answering
```01/06/2016, DL, compose-NN, RL, QA```
```Jacob Andreas, Marcus Rohrbach, Trevor Darrell, Dan Klein, NAACL 2016 (best-paper)```
* Asynchronous Methods for Deep Reinforcement Learning
```01/06/2016, DL, RL```
```Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy P. Lillicrap, Tim Harley, David Silver, Koray Kavukcuoglu, 2016```
* Density estimation using Real NVP
```01/06/2016, DL, latent space, image generation```
```Laurent Dinh, Jascha Sohl-Dickstein, Samy Bengio, Google Brain```
* Sparse Filtering
```01/06/2016, ML, DL, sparse```
```Jiquan Ngiam, Pang Wei Koh, Zhenghao Chen, Sonia Bhaskar, Andrew Y. Ng, NIPS 2011```
* Reinforcement Learning Neural Turing Machines
```31/05/2016, DL, NTM, RL```
```Wojciech Zaremba, ICLR 2016```
* Neural Networks with Few Multiplications
```31/05/2016, DL, optimization```
```Zhouhan Lin, Matthieu Courbariaux, Roland Memisevic, Yoshua Bengio, ICLR 2016```
* Recurrent neural network based language model
```30/05/2016, DL, NLP, RNN, language-model```
```Tomas Mikolov, Martin Karafiat, Lukas Burget, Jan Cernocky, Sanjeev Khudanpur, INTERSPEECH 2010```
* Ask Me Anything: Dynamic Memory Networks for Natural Language Processing
```30/05/2016, DL, DMN, NLP, dynamic-memory-networks```
```Ankit Kumar, Ozan Irsoy, Peter Ondruska, Mohit Iyyer, James Bradbury, Ishaan Gulrajani, Victor Zhong, Romain Paulus, Richard Socher, ICML 2016```
* Actor-Mimic : Deep Multitask and Transfer Reinforcement Learning
```30/05/2016, DL, RL, actor-mimic```
```Emilio Parisotto, Jimmy Ba, Ruslan Salakhutdinov, ICLR 2016```
* Neural Programmer-Interpreters
```29/05/2016, DL, NPI```
```Scott Reed, Nando de Freitas, ICLR 2016```
* End-to-End Training of Deep Visuomotor Policies
```29/05/2016, CV, DL, RL, robotics, control```
```Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel, JMLR 2016```
* MovieQA : Understanding Stories in Movies through Question-Answering
```28/05/2016, CV, DL, QA, movie-story```
```Makarand Tapaswi, Yukun Zhu, Rainer Stiefelhagen, Antonio Torralba, Raquel Urtasun, Sanja Fidler, CVPR 2016```
* Order-Embeddings of Images and Language
```28/05/2016, CV, DL, image-caption, hierarchy```
```Ivan Vendrov, Ryan Kiros, Sanja Fidler, Raquel Urtasun, ICLR 2016```
* Action Recognition using Visual Attention
```28/05/2016, CV, DL, action-recognition, attention```
```Shikhar Sharma, Ryan Kiros, Ruslan Salakhutdinov, ICLR 2016```
* End-To-End Memory Networks
```28/05/2016, DL, memory-networks, end-to-end```
```Sainbayar Sukhbaatar, Arthur Szlam, Jason Weston, Rob Fergus, NIPS 2015```
* Dueling Network Architectures for Deep Reinforcement Learning
```27/05/2016, DL, dueling-networks, RL```
```Ziyu Whang, Tom Schaul, Matteo Hessel, Hado van Hasselt, Marc Lanctot, Nando de Freitas, ICML 2016```
* Memory Networks
```27/05/2016, DL, memory-networks```
```Jason Weston, Sumit Chopra, Antoine Bordes, ICLR 2015```
* Playing Atari with Deep Reinforcement Learning
```27/05/2016, DL, DQN, RL```
```Volodymyr Mnih et al, NIPS DL workshop 2013```
* Deep Networks with Stochastic Depth
```27/05/2016, DL, stochastic-depth```
```Gao Huang, Yu Sun et al, 2016```
* An Introduction to Variational Methods for Graphical Models
```26/05/2016, graphical-models, ML, variational-methods```
```Michael Jordan et al, Machine Learning 1999```
* Deep Visual-Semantic Alignments for Generating Image Descriptions
```26/05/2016, CV, DL, image-captioning, NLP```
```Andrej Karpathy, Li Fei-Fei, CVPR 2015```
* VQA : Visual Question Answering
```26/05/2016, CV, DL, QA```
```Aishwarya Agarwal et al, ICCV 2015```
* Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks
```26/05/2016, DL, RNN, scheduled-sampling```
```Samy Bengio et al, NIPS 2015```
* Batch Normalization : Accelerating Deep Network Training by Reducing Covariate Shift
```26/05/2016, batch-norm, DL```
```Sergey Ioffe, Christian Szegedy, JMLR 2015```
* Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
```25/05/2016, CV, DL, attention, caption```
```Kelvin Xu et al, JMLR 2015```
* Pointer Networks
```24/05/2016, DL, Pointer-Nets```
```Oriol Vinyals, Meire Fortunato, Navdeep Jaitly, NIPS 2015```
* Order Matters : Sequence to Sequence for sets
```24/05/2016, DL, seq2seq, ordered, sorting```
```Oriol Vinyals, Samy Bengio, Manjunath Kudlur, ICLR 2016```
* Neural Machine Translation by Jointly Learning to Align and Translate
```23/05/2016, DL, NMT```
```Bahdanau, Cho, Bengio, ICLR 2015```
* Visualizing Data using t-SNE
```23/05/2016, ML, Embeddings```
```Maaten, Hinton,, JMLR 2008```
* Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
```22/05/2016, DL, NLP, RNN-ED```
```Kyunghyun Cho et al, ACL 2014```
* Deep Residual Learning for Image Recognition
```22/05/2016, CV, DL, ResNets```
```Kaiming He et al, 2015```
* Neural Turing Machines
```22/05/2016, DL, NTM```
```Alex Graves et al, 2014```
* Support Vector Machine Learning for Interdependent and Structured Output Spaces
```21/05/2016, ML, StructSVM```
```Ioannis Tsochantaridis et al, ICML 2004```
* Generative Adversarial Networks
```21/05/2016, DL, GAN, generative```
```Ian Goodfellow et al, NIPS 2014```
* Sequence to Sequence learning with neural networks
```21/05/2016, DL, Seq2Seq```
```Ilya Sutskever, Oriol Vinyals, and Quoc Le, NIPS 2014```
* Adam : A Method for Stochastic Optimization
```20/05/2016, ML, Optimization, ADAM```
```Diederik Kingma, Jimmy Ba, ICLR 2015```
* Neural GPUs Learn Algorithms
```20/05/2016, DL```
```Lukasz Kaiser, Ilya Sutskever, ICLR 2016```
* Generating Sequences With Recurrent Neural Networks
```19/05/2016, DL```
```Alex Graves, 2014```
* Generative Adversarial Text to Image Synthesis
```19/05/2016, CV, DL```
```Scott Reed et al, ICML 2016```
* Learning word embeddings efficiently with noise-contrastive estimation
```18/05/2016, DL, NLP```
```Andriy Mnih et al, NIPS 2013```
* Generating Sentences from a Continuous Space
```18/05/2016, DL, NLP```
```Samuel Bowman et al, 2015```
* Reinforcement Learning: A Survey
```18/05/2016, AI, ML, RL```
```Leslie Kaebling et al, JAIR 1996```
* Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
```18/05/2016, AI, ML, REINFORCE```
```Ronald Williams, Machine Learning 1992```
* Training Neural Networks Without Gradients: A Scalable ADMM Approach
```17/05/2016, DL```
```Gavin Taylor et al, ICML 2016```
* k-means++: The Advantages of Careful Seeding
```17/05/2016, Clustering, ML```
```David Arthur et al, SODA 2007```
* Bridging the Gaps Betweeen Residual Learning, Recurrent Neural Networks and Visual Cortex
```13/04/2016, CV, DL, cortex```
```Quanli Liao, Tomas Poggio, arxiv```
* Eye movements in natural behaviour
```01/06/2015, eye-movement, attention```
```Mary Hayhoe, Dana Ballard, Trends in Cognitive Sciences, 2005```
* Optimizing Neural Networks with Kronecker-factored Approximate Curvature
```19/03/2015, K-FAC```
```James Martens, Roger Grosse, ICML 2015```
* Randomized Nonlinear Component Analysis
```13/05/2014, RNCA, ML```
```David Lopez-Paz, Suvrit Sra, Alex Smola, Zoubin Grahramani, Bernhard Scholkopf, ICML, 2014```
* On Information and Sufficiency
```01/03/1951, classics, KL-divergence, information theory```
```S. Kullback and R. A. Leibler, The Annals of Mathematical Statistics``` \[Review\]