ICLR 2016
80 papers
A Note on the Evaluation of Generative Models
Lucas Theis, Aäron van den Oord, Matthias Bethge A Test of Relative Similarity for Model Selection in Generative Models
Wacha Bounliphone, Eugene Belilovsky, Matthew B. Blaschko, Ioannis Antonoglou, Arthur Gretton ACDC: A Structured Efficient Linear Layer
Marcin Moczulski, Misha Denil, Jeremy Appleyard, Nando de Freitas Adversarial Manipulation of Deep Representations
Sara Sabour, Yanshuai Cao, Fartash Faghri, David J. Fleet All You Need Is a Good Init
Dmytro Mishkin, Jiri Matas Continuous Control with Deep Reinforcement Learning
Timothy P. Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, Daan Wierstra Data-Dependent Initializations of Convolutional Neural Networks
Philipp Krähenbühl, Carl Doersch, Jeff Donahue, Trevor Darrell Data-Dependent Path Normalization in Neural Networks
Behnam Neyshabur, Ryota Tomioka, Ruslan Salakhutdinov, Nathan Srebro Deep Linear Discriminant Analysis
Matthias Dorfer, Rainer Kelz, Gerhard Widmer Distributional Smoothing by Virtual Adversarial Examples
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, Ken Nakae, Shin Ishii Diversity Networks
Zelda Mariet, Suvrit Sra Evaluating Prerequisite Qualities for Learning End-to-End Dialog Systems
Jesse Dodge, Andreea Gane, Xiang Zhang, Antoine Bordes, Sumit Chopra, Alexander H. Miller, Arthur Szlam, Jason Weston Gated Graph Sequence Neural Networks
Yujia Li, Daniel Tarlow, Marc Brockschmidt, Richard S. Zemel Generating Images from Captions with Attention
Elman Mansimov, Emilio Parisotto, Lei Jimmy Ba, Ruslan Salakhutdinov Geodesics of Learned Representations
Olivier J. Hénaff, Eero P. Simoncelli Grid Long Short-Term Memory
Nal Kalchbrenner, Ivo Danihelka, Alex Graves High-Dimensional Continuous Control Using Generalized Advantage Estimation
John Schulman, Philipp Moritz, Sergey Levine, Michael I. Jordan, Pieter Abbeel Importance Weighted Autoencoders
Yuri Burda, Roger B. Grosse, Ruslan Salakhutdinov Learning to Diagnose with LSTM Recurrent Neural Networks
Zachary Chase Lipton, David C. Kale, Charles Elkan, Randall C. Wetzel Learning Visual Predictive Models of Physics for Playing Billiards
Katerina Fragkiadaki, Pulkit Agrawal, Sergey Levine, Jitendra Malik Metric Learning with Adaptive Density Discrimination
Oren Rippel, Manohar Paluri, Piotr Dollár, Lubomir D. Bourdev Multi-Task Sequence to Sequence Learning
Minh-Thang Luong, Quoc V. Le, Ilya Sutskever, Oriol Vinyals, Lukasz Kaiser Neural GPUs Learn Algorithms
Lukasz Kaiser, Ilya Sutskever Neural Networks with Few Multiplications
Zhouhan Lin, Matthieu Courbariaux, Roland Memisevic, Yoshua Bengio Neural Programmer-Interpreters
Scott E. Reed, Nando de Freitas Neural Random-Access Machines
Karol Kurach, Marcin Andrychowicz, Ilya Sutskever Order Matters: Sequence to Sequence for Sets
Oriol Vinyals, Samy Bengio, Manjunath Kudlur Order-Embeddings of Images and Language
Ivan Vendrov, Ryan Kiros, Sanja Fidler, Raquel Urtasun Policy Distillation
Andrei A. Rusu, Sergio Gomez Colmenarejo, Çaglar Gülçehre, Guillaume Desjardins, James Kirkpatrick, Razvan Pascanu, Volodymyr Mnih, Koray Kavukcuoglu, Raia Hadsell Prioritized Experience Replay
Tom Schaul, John Quan, Ioannis Antonoglou, David Silver Reasoning About Entailment with Neural Attention
Tim Rocktäschel, Edward Grefenstette, Karl Moritz Hermann, Tomás Kociský, Phil Blunsom Reasoning in Vector Space: An Exploratory Study of Question Answering
Moontae Lee, Xiaodong He, Wen-tau Yih, Jianfeng Gao, Li Deng, Paul Smolensky Recurrent Gaussian Processes
César Lincoln C. Mattos, Zhenwen Dai, Andreas C. Damianou, Jeremy Forth, Guilherme A. Barreto, Neil D. Lawrence Reducing Overfitting in Deep Networks by Decorrelating Representations
Michael Cogswell, Faruk Ahmed, Ross B. Girshick, Larry Zitnick, Dhruv Batra Segmental Recurrent Neural Networks
Lingpeng Kong, Chris Dyer, Noah A. Smith Sequence Level Training with Recurrent Neural Networks
Marc'Aurelio Ranzato, Sumit Chopra, Michael Auli, Wojciech Zaremba Session-Based Recommendations with Recurrent Neural Networks
Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, Domonkos Tikk SparkNet: Training Deep Networks in Spark
Philipp Moritz, Robert Nishihara, Ion Stoica, Michael I. Jordan The Variational Fair Autoencoder
Christos Louizos, Kevin Swersky, Yujia Li, Max Welling, Richard S. Zemel Towards Universal Paraphrastic Sentence Embeddings
John Wieting, Mohit Bansal, Kevin Gimpel, Karen Livescu Training CNNs with Low-Rank Filters for Efficient Image Classification
Yani Ioannou, Duncan P. Robertson, Jamie Shotton, Roberto Cipolla, Antonio Criminisi Unifying Distillation and Privileged Information
David Lopez-Paz, Léon Bottou, Bernhard Schölkopf, Vladimir Vapnik Variable Rate Image Compression with Recurrent Neural Networks
George Toderici, Sean M. O'Malley, Sung Jin Hwang, Damien Vincent, David Minnen, Shumeet Baluja, Michele Covell, Rahul Sukthankar Variational Auto-Encoded Deep Gaussian Processes
Zhenwen Dai, Andreas C. Damianou, Javier González, Neil D. Lawrence Variational Gaussian Process
Dustin Tran, Rajesh Ranganath, David M. Blei