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ICLR 2016: San Juan, Puerto Rico
- Yoshua Bengio, Yann LeCun:
4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings. 2016
Oral Presentations
- Scott E. Reed, Nando de Freitas:
Neural Programmer-Interpreters. - David Krueger, Roland Memisevic:
Regularizing RNNs by Stabilizing Activations. - Shihao Ji
, S. V. N. Vishwanathan, Nadathur Satish, Michael J. Anderson, Pradeep Dubey:
BlackOut: Speeding up Recurrent Neural Network Language Models With Very Large Vocabularies. - Felix Hill, Antoine Bordes, Sumit Chopra, Jason Weston:
The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations. - John Wieting, Mohit Bansal, Kevin Gimpel, Karen Livescu:
Towards Universal Paraphrastic Sentence Embeddings. - Yixuan Li, Jason Yosinski, Jeff Clune, Hod Lipson, John E. Hopcroft:
Convergent Learning: Do different neural networks learn the same representations? - Tianqi Chen, Ian J. Goodfellow, Jonathon Shlens:
Net2Net: Accelerating Learning via Knowledge Transfer. - Dustin Tran, Rajesh Ranganath, David M. Blei:
Variational Gaussian Process. - Christos Louizos, Kevin Swersky, Yujia Li, Max Welling, Richard S. Zemel:
The Variational Fair Autoencoder. - Lucas Theis, Aäron van den Oord, Matthias Bethge:
A note on the evaluation of generative models. - Song Han, Huizi Mao, William J. Dally:
Deep Compression: Compressing Deep Neural Network with Pruning, Trained Quantization and Huffman Coding. - Zhouhan Lin, Matthieu Courbariaux, Roland Memisevic, Yoshua Bengio:
Neural Networks with Few Multiplications. - Ivan Vendrov, Ryan Kiros, Sanja Fidler, Raquel Urtasun:
Order-Embeddings of Images and Language. - Elman Mansimov, Emilio Parisotto, Lei Jimmy Ba, Ruslan Salakhutdinov:
Generating Images from Captions with Attention. - Johannes Ballé, Valero Laparra, Eero P. Simoncelli:
Density Modeling of Images using a Generalized Normalization Transformation.
Poster Presentations
- Fisher Yu, Vladlen Koltun:
Multi-Scale Context Aggregation by Dilated Convolutions. - Zachary Chase Lipton, David C. Kale, Charles Elkan, Randall C. Wetzel:
Learning to Diagnose with LSTM Recurrent Neural Networks. - Tom Schaul, John Quan, Ioannis Antonoglou, David Silver:
Prioritized Experience Replay. - Yuri Burda, Roger B. Grosse, Ruslan Salakhutdinov:
Importance Weighted Autoencoders. - Zhenwen Dai, Andreas C. Damianou, Javier González, Neil D. Lawrence
Variational Auto-encoded Deep Gaussian Processes. - Yani Ioannou
Training CNNs with Low-Rank Filters for Efficient Image Classification. - Michael Cogswell, Faruk Ahmed, Ross B. Girshick, Larry Zitnick, Dhruv Batra:
Reducing Overfitting in Deep Networks by Decorrelating Representations. - Iasonas Kokkinos:
Surpassing Humans in Boundary Detection using Deep Learning. - Tim Rocktäschel, Edward Grefenstette, Karl Moritz Hermann, Tomás Kociský, Phil Blunsom:
Reasoning about Entailment with Neural Attention. - Cheng Tai, Tong Xiao, Xiaogang Wang, Weinan E:
Convolutional neural networks with low-rank regularization. - David Lopez-Paz, Léon Bottou, Bernhard Schölkopf, Vladimir Vapnik:
Unifying distillation and privileged information. - Giorgos Tolias, Ronan Sicre, Hervé Jégou:
Particular object retrieval with integral max-pooling of CNN activations. - Dmytro Mishkin, Jiri Matas:
All you need is a good init. - Theofanis Karaletsos, Serge J. Belongie, Gunnar Rätsch:
When crowds hold privileges: Bayesian unsupervised representation learning with oracle constraints. - Arvind Neelakantan, Quoc V. Le, Ilya Sutskever:
Neural Programmer: Inducing Latent Programs with Gradient Descent. - Philipp Moritz, Robert Nishihara, Ion Stoica, Michael I. Jordan:
SparkNet: Training Deep Networks in Spark. - Jost Tobias Springenberg:
Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks. - Shixiang Gu, Sergey Levine, Ilya Sutskever, Andriy Mnih:
MuProp: Unbiased Backpropagation for Stochastic Neural Networks. - Hristo S. Paskov, John C. Mitchell, Trevor J. Hastie:
Data Representation and Compression Using Linear-Programming Approximations. - Zelda Mariet, Suvrit Sra:
Diversity Networks. - Matthew J. Hausknecht, Peter Stone:
Deep Reinforcement Learning in Parameterized Action Space. - Katerina Fragkiadaki, Pulkit Agrawal, Sergey Levine, Jitendra Malik:
Learning Visual Predictive Models of Physics for Playing Billiards. - Jason Weston, Antoine Bordes, Sumit Chopra, Tomás Mikolov:
Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks. - Jesse Dodge, Andreea Gane, Xiang Zhang, Antoine Bordes, Sumit Chopra, Alexander H. Miller, Arthur Szlam, Jason Weston:
Evaluating Prerequisite Qualities for Learning End-to-End Dialog Systems. - Yuandong Tian, Yan Zhu:
Better Computer Go Player with Neural Network and Long-term Prediction. - Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, Ken Nakae, Shin Ishii:
Distributional Smoothing by Virtual Adversarial Examples. - Minh-Thang Luong, Quoc V. Le, Ilya Sutskever, Oriol Vinyals, Lukasz Kaiser:
Multi-task Sequence to Sequence Learning. - Wacha Bounliphone, Eugene Belilovsky, Matthew B. Blaschko, Ioannis Antonoglou, Arthur Gretton:
A Test of Relative Similarity For Model Selection in Generative Models. - Yong-Deok Kim, Eunhyeok Park, Sungjoo Yoo, Taelim Choi, Lu Yang, Dongjun Shin:
Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications. - Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, Domonkos Tikk:
Session-based Recommendations with Recurrent Neural Networks. - Timothy P. Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, Daan Wierstra:
Continuous control with deep reinforcement learning. - César Lincoln C. Mattos, Zhenwen Dai, Andreas C. Damianou, Jeremy Forth, Guilherme A. Barreto, Neil D. Lawrence
Recurrent Gaussian Processes. - Stefano Soatto, Alessandro Chiuso:
Modeling Visual Representations: Defining Properties and Deep Approximations. - Samaneh Azadi, Jiashi Feng, Stefanie Jegelka, Trevor Darrell:
Auxiliary Image Regularization for Deep CNNs with Noisy Labels. - Andrei A. Rusu, Sergio Gomez Colmenarejo, Çaglar Gülçehre, Guillaume Desjardins, James Kirkpatrick, Razvan Pascanu, Volodymyr Mnih, Koray Kavukcuoglu, Raia Hadsell:
Policy Distillation. - Karol Kurach, Marcin Andrychowicz, Ilya Sutskever:
Neural Random-Access Machines. - Yujia Li, Daniel Tarlow, Marc Brockschmidt, Richard S. Zemel:
Gated Graph Sequence Neural Networks. - Oren Rippel, Manohar Paluri, Piotr Dollár, Lubomir D. Bourdev:
Metric Learning with Adaptive Density Discrimination. - Harrison Edwards, Amos J. Storkey:
Censoring Representations with an Adversary. - George Toderici, Sean M. O'Malley, Sung Jin Hwang, Damien Vincent, David Minnen, Shumeet Baluja, Michele Covell, Rahul Sukthankar:
Variable Rate Image Compression with Recurrent Neural Networks. - Nicolas Ballas, Li Yao, Chris Pal, Aaron C. Courville:
Delving Deeper into Convolutional Networks for Learning Video Representations. - Tim Dettmers:
8-Bit Approximations for Parallelism in Deep Learning. - Philipp Krähenbühl, Carl Doersch, Jeff Donahue, Trevor Darrell:
Data-dependent Initializations of Convolutional Neural Networks. - Oriol Vinyals, Samy Bengio, Manjunath Kudlur:
Order Matters: Sequence to sequence for sets. - John Schulman, Philipp Moritz, Sergey Levine, Michael I. Jordan, Pieter Abbeel:
High-Dimensional Continuous Control Using Generalized Advantage Estimation. - Michaël Mathieu, Camille Couprie, Yann LeCun:
Deep multi-scale video prediction beyond mean square error. - Nal Kalchbrenner, Ivo Danihelka, Alex Graves:
Grid Long Short-Term Memory. - Yann N. Dauphin, David Grangier:
Predicting distributions with Linearizing Belief Networks. - Djork-Arné Clevert, Thomas Unterthiner, Sepp Hochreiter
Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs). - Emilio Parisotto, Lei Jimmy Ba, Ruslan Salakhutdinov:
Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning. - Lingpeng Kong, Chris Dyer, Noah A. Smith:
Segmental Recurrent Neural Networks. - Matthias Dorfer, Rainer Kelz, Gerhard Widmer:
Deep Linear Discriminant Analysis. - Weiran Wang, Karen Livescu:
Large-Scale Approximate Kernel Canonical Correlation Analysis. - Alec Radford, Luke Metz, Soumith Chintala:
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. - Pouya Bashivan, Irina Rish, Mohammed Yeasin, Noel Codella:
Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks. - Amr Bakry, Mohamed Elhoseiny, Tarek El-Gaaly, Ahmed M. Elgammal:
Digging Deep into the Layers of CNNs: In Search of How CNNs Achieve View Invariance. - Alexandre de Brébisson, Pascal Vincent:
An Exploration of Softmax Alternatives Belonging to the Spherical Loss Family. - Behnam Neyshabur, Ryota Tomioka, Ruslan Salakhutdinov, Nathan Srebro:
Data-Dependent Path Normalization in Neural Networks. - Moontae Lee, Xiaodong He, Wen-tau Yih, Jianfeng Gao, Li Deng, Paul Smolensky:
Reasoning in Vector Space: An Exploratory Study of Question Answering. - Lukasz Kaiser
Neural GPUs Learn Algorithms. - Marcin Moczulski, Misha Denil, Jeremy Appleyard, Nando de Freitas:
ACDC: A Structured Efficient Linear Layer. - Sara Sabour, Yanshuai Cao, Fartash Faghri, David J. Fleet:
Adversarial Manipulation of Deep Representations. - Olivier J. Hénaff, Eero P. Simoncelli:
Geodesics of learned representations. - Marc'Aurelio Ranzato, Sumit Chopra, Michael Auli, Wojciech Zaremba:
Sequence Level Training with Recurrent Neural Networks. - Joan Bruna, Pablo Sprechmann, Yann LeCun:
Super-Resolution with Deep Convolutional Sufficient Statistics.
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