ICLR 2016: List of accepted papers:

   (Update 2: If you are looking for the ICLR 2017 papers, they are in open review here )

(update: All the blog entries related to the ICLR conferences are filed under the ICLR tag. )

From Hugo's twitter feed:

Accepted papers to the ICLR 2016 Conference Track are now available here:https://t.co/T9zsHkS8Pe

— Hugo Larochelle (@hugo_larochelle) February 8, 2016

Here is the list of accepted papers:

• ACDC: A Structured Efficient Linear Layer Marcin Moczulski, Misha Denil, Jeremy Appleyard, Nando de Freitas 
• Neural Networks with Few Multiplications Zhouhan Lin, Matthieu Courbariaux, Roland Memisevic, Yoshua Bengio  
• Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding Song Han, Huizi Mao, Bill Dally 
• Unifying distillation and privileged information David Lopez-Paz, Leon Bottou, Bernhard Schölkopf, Vladimir Vapnik  
• Data Representation and Compression Using Linear-Programming Approximations Hristo Paskov, John Mitchell, Trevor Hastie
• Multi-Scale Context Aggregation by Dilated Convolutions Fisher Yu, Vladlen Koltun
• The Variational Fair Autoencoder Christos Louizos, Kevin Swersky, Yujia Li, Max Welling, Richard Zemel
• A note on the evaluation of generative models Lucas Theis, Aäron van den Oord, Matthias Bethge
• Learning to Diagnose with LSTM Recurrent Neural Networks Zachary Lipton, David Kale, Charles Elkan, Randall Wetzel
• Prioritized Experience Replay Tom Schaul, John Quan, Ioannis Antonoglou, David Silver
• Importance Weighted Autoencoders Yuri Burda, Ruslan Salakhutdinov, Roger Grosse
• Variationally Auto-Encoded Deep Gaussian Processes Zhenwen Dai, Andreas Damianou, Javier Gonzalez, Neil Lawrence
• Training Convolutional Neural Networks with Low-rank Filters for Efficient Image Classification Yani Ioannou, Duncan Robertson, Jamie Shotton, roberto Cipolla, Antonio Criminisi, Jamie Shotton
• Reducing Overfitting in Deep Networks by Decorrelating Representations Michael Cogswell, Faruk Ahmed, Ross Girshick, Larry Zitnick, Dhruv Batra
• Pushing the Boundaries of Boundary Detection using Deep Learning Iasonas Kokkinos
• Generating Images from Captions with Attention Elman Mansimov, Emilio Parisotto, Jimmy Ba, Ruslan Salakhutdinov
• Reasoning about Entailment with Neural Attention Tim Rocktäschel, Edward Grefenstette, Karl Moritz Hermann, Tomáš Kočiský, Phil Blunsom
• Convolutional Neural Networks With Low-rank Regularization Cheng Tai, Tong Xiao, Yi Zhang, Xiaogang Wang, Weinan E
• Particular object retrieval with integral max-pooling of CNN activations Giorgos Tolias, Ronan Sicre, Hervé Jégou
• All you need is a good init Dmytro Mishkin, Jiri Matas
• Bayesian Representation Learning with Oracle Constraints Theofanis Karaletsos, Serge Belongie, Gunnar Rätsch
• Neural Programmer: Inducing Latent Programs with Gradient Descent Arvind Neelakantan, Quoc Le, Ilya Sutskever
• Towards Universal Paraphrastic Sentence Embeddings John Wieting, Mohit Bansal, Kevin Gimpel, Karen Livescu
• Regularizing RNNs by Stabilizing Activations David Krueger, Roland Memisevic
• SparkNet: Training Deep Networks in Spark Philipp Moritz, Robert Nishihara, Ion Stoica, Michael Jordan
• Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks Jost Tobias Springenberg
• The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations Felix Hill, Antoine Bordes, Sumit Chopra, Jason Weston
• MuProp: Unbiased Backpropagation For Stochastic Neural Networks Shixiang Gu, Sergey Levine, Ilya Sutskever, Andriy Mnih
• Diversity Networks Zelda Mariet, Suvrit Sra
• Deep Reinforcement Learning in Parameterized Action Space Matthew Hausknecht, Peter Stone
• Learning VIsual Predictive Models of Physics for Playing Billiards Katerina Fragkiadaki, Pulkit Agrawal, Sergey Levine, Jitendra Malik
• Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks Jason Weston, Antoine Bordes, Sumit Chopra, Sasha Rush, Bart van Merrienboer, Armand Joulin, Tomas Mikolov
• Evaluating Prerequisite Qualities for Learning End-to-end Dialog Systems Jesse Dodge, Andreea Gane, Xiang Zhang, Antoine Bordes, Sumit Chopra, Alexander Miller, Arthur Szlam, Jason Weston
• Better Computer Go Player with Neural Network and Long-term Prediction Yuandong Tian, Yan Zhu
• Distributional Smoothing with Virtual Adversarial Training Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, Ken Nakae, Shin Ishii
• Multi-task Sequence to Sequence Learning Minh-Thang Luong, Quoc Le, Ilya Sutskever, Oriol Vinyals, Lukasz Kaiser
• A Test of Relative Similarity for Model Selection in Generative Models Eugene Belilovsky, Wacha Bounliphone, Matthew Blaschko, Ioannis Antonoglou, Arthur Gretton
• Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications Yong-Deok Kim, Eunhyeok Park, Sungjoo Yoo, Taelim Choi, Lu Yang, Dongjun Shin
• Neural Programmer-Interpreters Scott Reed, Nando de Freitas
• Session-based recommendations with recurrent neural networks Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, Domonkos Tikk
• Continuous control with deep reinforcement learning Timothy Lillicrap, Jonathan Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, Daan Wierstra
• Recurrent Gaussian Processes César Lincoln Mattos, Zhenwen Dai, Andreas Damianou, Jeremy Forth, Guilherme Barreto, Neil Lawrence
• Modeling Visual Representations:Defining Properties and Deep Approximations Stefano Soatto, Alessandro Chiuso
• Auxiliary Image Regularization for Deep CNNs with Noisy Labels Samaneh Azadi, Jiashi Feng, Stefanie Jegelka, Trevor Darrell
• Convergent Learning: Do different neural networks learn the same representations? Yixuan Li, Jason Yosinski, Jeff Clune, Hod Lipson, John Hopcroft
• Policy Distillation Andrei Rusu, Sergio Gomez, Caglar Gulcehre, Guillaume Desjardins, James Kirkpatrick, Razvan Pascanu, Volodymyr Mnih, Koray Kavukcuoglu, Raia Hadsell
• Neural Random-Access Machines Karol Kurach, Marcin Andrychowicz, Ilya Sutskever
• Gated Graph Sequence Neural Networks Yujia Li, Daniel Tarlow, Marc Brockschmidt, Richard Zemel, CIFAR
• Metric Learning with Adaptive Density Discrimination Oren Rippel, Manohar Paluri, Piotr Dollar, Lubomir Bourdev
• Censoring Representations with an Adversary Harrison Edwards, Amos Storkey
• Order-Embeddings of Images and Language Ivan Vendrov, Ryan Kiros, Sanja Fidler, Raquel Urtasun
• Variable Rate Image Compression with Recurrent Neural Networks George Toderici, Sean O'Malley, Damien Vincent, Sung Jin Hwang, Michele Covell, Shumeet Baluja, Rahul Sukthankar, David Minnen
• Delving Deeper into Convolutional Networks for Learning Video Representations nicolas Ballas, Li Yao, Pal Chris, Aaron Courville
• 8-Bit Approximations for Parallelism in Deep Learning Tim Dettmers
• Data-dependent initializations of Convolutional Neural Networks Philipp Kraehenbuehl, Carl Doersch, Jeff Donahue, Trevor Darrell
• Order Matters: Sequence to sequence for sets Oriol Vinyals, Samy Bengio, Manjunath Kudlur
• High-Dimensional Continuous Control Using Generalized Advantage Estimation John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, Pieter Abbeel
• BlackOut: Speeding up Recurrent Neural Network Language Models With Very Large Vocabularies Shihao Ji, Swaminathan Vishwanathan, Nadathur Satish, Michael Anderson, Pradeep Dubey
• Deep Multi Scale Video Prediction Beyond Mean Square Error Michael Mathieu, Camille couprie, Yann Lecun
• Grid Long Short-Term Memory Nal Kalchbrenner, Alex Graves, Ivo Danihelka
• Net2Net: Accelerating Learning via Knowledge Transfer Tianqi Chen, Ian Goodfellow, Jon Shlens
• Predicting distributions with Linearizing Belief Networks Yann Dauphin, David Grangier
• Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) Djork-Arné Clevert, Thomas Unterthiner, Sepp Hochreiter
• Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning Emilio Parisotto, Jimmy Ba, Ruslan Salakhutdinov
• Segmental Recurrent Neural Networks Lingpeng Kong, Chris Dyer, Noah Smith
• Deep Linear Discriminant Analysis Matthias Dorfer, Rainer Kelz, Gerhard Widmer
• Large-Scale Approximate Kernel Canonical Correlation Analysis Weiran Wang, Karen Livescu
• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Alec Radford, Luke Metz, Soumith Chintala
• Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks Pouya Bashivan, Irina Rish, Mohammed Yeasin, Noel Codella
• Digging Deep into the layers of CNNs: In Search of How CNNs Achieve View Invariance Amr Bakry, Mohamed Elhoseiny, Tarek El-Gaaly, Ahmed Elgammal
• An Exploration of Softmax Alternatives Belonging to the Spherical Loss Family Alexandre De Brébisson, Pascal Vincent
• Data-Dependent Path Normalization in Neural Networks Behnam Neyshabur, Ryota Tomioka, Ruslan Salakhutdinov, Nathan Srebro
• Reasoning in Vector Space: An Exploratory Study of Question Answering Moontae Lee, Xiaodong He, Wen-tau Yih, Jianfeng Gao, Li Deng, Paul Smolensky
• Neural GPUs Learn Algorithms Lukasz Kaiser, Ilya Sutskever
• Density Modeling of Images using a Generalized Normalization Transformation Johannes Ballé, Valero Laparra, Eero Simoncelli
• Adversarial Manipulation of Deep Representations Sara Sabour, Yanshuai Cao, Fartash Faghri, David Fleet
• Geodesics of learned representations Olivier Hénaff, Eero Simoncelli
• Sequence Level Training with Recurrent Neural Networks Marc'Aurelio Ranzato, Sumit Chopra, Michael Auli, Wojciech Zaremba
• Super-resolution with deep convolutional sufficient statistics joan bruna, Pablo Sprechmann, Yann Lecun
• Variational Gaussian Process Dustin Tran, Rajesh Ranganath, David Blei

Pluto’s Mysterious, Floating Hills

Release Date: February 4, 2016

Keywords: MVIC, Pluto, Ralph

Credit: NASA/Johns Hopkins University Applied Physics Laboratory/Southwest Research Institute

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