ACCEPTED PAPERS

ORALS

Boldface denotes the presenter.

Learning Representations for Counterfactual Inference
Fredrik D. Johansson, Uri Shalit, David Sontag
https://arxiv.org/pdf/1605.03661v2.pdf

Sequence-to-Sequence Learning as Beam-Search Optimization
Sam Wiseman and Alexander M. Rush
https://arxiv.org/pdf/1606.02960v1.pdf

Deep Networks with Stochastic Depth
Gao Huang, Yu Sun, Zhuang Liu, Daniel Sedra, Kilian Q. Weinberger
https://arxiv.org/pdf/1603.09382v3.pdf

InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, Pieter Abbeel
https://arxiv.org/pdf/1606.03657v1.pdf

Conditional Image Generation with PixelCNN Decoders
Aaron van den Oord, Nal Kalchbrenner, Oriol Vinyals, Lasse Espeholt, Alex Graves, Koray Kavukcuoglu
https://arxiv.org/pdf/1606.05328v2.pdf

Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization
Chelsea Finn, Sergey Levine, Pieter Abbeel
https://arxiv.org/pdf/1603.00448v3.pdf

Density estimation using Real NVP
Laurent Dinh, Jascha Sohl-Dickstein, Samy Bengio
https://arxiv.org/pdf/1605.08803v1.pdf

Layer Normalization
Jimmy Lei Ba, Jamie Ryan Kiros, Geoffrey E. Hinton
https://arxiv.org/pdf/1607.06450v1.pdf


POSTERS

Adversarially learned inference
Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Alex Lamb, Martin Arjovsky
https://arxiv.org/pdf/1606.00704v1.pdf

Adversarial feature learning
Jeff Donahue, Philipp Krähenbühl, Trevor Darrell

Learning to learn by gradient descent by gradient descent
Marcin Andrychowicz, Misha Denil, Sergio Gomez, Matthew W. Hoffman, David Pfau, Tom Schaul, Nando de Freitas
https://arxiv.org/pdf/1606.04474v1.pdf

Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, Victor Lempitsky
https://arxiv.org/pdf/1505.07818v4.pdf

Improving Variational Inference with Inverse Autoregressive Flow
Diederik P Kingma, Tim Salimans, Max Welling
https://arxiv.org/pdf/1606.04934v1.pdf

Synthesizing the preferred inputs for neurons in neural networks via deep generator networks
Anh Nguyen, Alexey Dosovitskiy, Jason Yosinski, Thomas Brox, Jeff Clune
http://arxiv.org/pdf/1605.09304v3.pdf

One-shot learning with memory-augmented neural networks
Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, Timothy Lillicrap
https://arxiv.org/pdf/1605.06065v1.pdf

Matching networks for one-shot learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Koray Kavukcuoglu, Daan Wierstra
https://arxiv.org/pdf/1606.04080v1.pdf

Structured Prediction Energy Networks
David Belanger, Andrew McCallum
https://arxiv.org/pdf/1511.06350v3.pdf

Learning multiagent communication with backpropagation
Sainbayar Sukhbaatar, Arthur Szlam, Rob Fergus
http://arxiv.org/pdf/1605.07736v1.pdf

End to end learning for self-driving cars
Mariusz Bojarski, Davide Del Testa, Daniel Dworakowski, Bernhard Firner, Beat Flepp, Prasoon Goyal, Lawrence D. Jackel, Mathew Monfort, Urs Muller, Jiakai Zhang, Xin Zhang, Jake Zhao, Karol Zieba
https://arxiv.org/pdf/1604.07316v1.pdf

Terrain-adaptive locomotion skills using deep reinforcement learning
Xue Bin Peng, Glen Berseth, Michiel van de Panne
https://www.cs.ubc.ca/~van/papers/2016-TOG-deepRL/2016-TOG-deepRL.pdf

Towards Conceptual Compression
Karol Gregor, Frederic Besse, Danilo Jimenez Rezende, Ivo Danihelka, Daan Wierstra
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