Get our free extension to see links to code for papers anywhere online!

Chrome logo  Add to Chrome

Firefox logo Add to Firefox

Interpreting Spatially Infinite Generative Models


Jul 24, 2020
Chaochao Lu, Richard E. Turner, Yingzhen Li, Nate Kushman

* ICML 2020 workshop on Human Interpretability in Machine Learning (WHI 2020) 

   Access Paper or Ask Questions

  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

Inverse Graphics GAN: Learning to Generate 3D Shapes from Unstructured 2D Data


Feb 28, 2020
Sebastian Lunz, Yingzhen Li, Andrew Fitzgibbon, Nate Kushman

* 8 pages paper, 3 pages references, 18 pages appendix 

   Access Paper or Ask Questions

  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

Learning Robust Representations via Multi-View Information Bottleneck


Feb 18, 2020
Marco Federici, Anjan Dutta, Patrick Forré, Nate Kushman, Zeynep Akata


   Access Paper or Ask Questions

  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

Constructing Unrestricted Adversarial Examples with Generative Models


Sep 20, 2018
Yang Song, Rui Shu, Nate Kushman, Stefano Ermon

* NIPS 2018 

   Access Paper or Ask Questions

  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

Inverting Supervised Representations with Autoregressive Neural Density Models


Jun 01, 2018
Charlie Nash, Nate Kushman, Christopher K. I. Williams


   Access Paper or Ask Questions

  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples


May 21, 2018
Yang Song, Taesup Kim, Sebastian Nowozin, Stefano Ermon, Nate Kushman

* ICLR 2018 

   Access Paper or Ask Questions

  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

A Deep Learning Approach for Joint Video Frame and Reward Prediction in Atari Games


Aug 17, 2017
Felix Leibfried, Nate Kushman, Katja Hofmann

* Presented at the ICML 2017 Workshop on Principled Approaches to Deep Learning, Sydney, Australia, 2017 

   Access Paper or Ask Questions

  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

Differentiable Programs with Neural Libraries


Mar 02, 2017
Alexander L. Gaunt, Marc Brockschmidt, Nate Kushman, Daniel Tarlow


   Access Paper or Ask Questions

  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

Summary - TerpreT: A Probabilistic Programming Language for Program Induction


Dec 02, 2016
Alexander L. Gaunt, Marc Brockschmidt, Rishabh Singh, Nate Kushman, Pushmeet Kohli, Jonathan Taylor, Daniel Tarlow

* 7 pages, 2 figures, 4 tables in 1st Workshop on Neural Abstract Machines & Program Induction (NAMPI), @NIPS 2016 

   Access Paper or Ask Questions

  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

TerpreT: A Probabilistic Programming Language for Program Induction


Aug 15, 2016
Alexander L. Gaunt, Marc Brockschmidt, Rishabh Singh, Nate Kushman, Pushmeet Kohli, Jonathan Taylor, Daniel Tarlow

* 50 pages, 20 figures, 4 tables 

   Access Paper or Ask Questions

  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email
1
2
>>