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

 Add to Chrome

 Add to Firefox

CatalyzeX Code Finder - Browser extension linking code for ML papers across the web! | Product Hunt Embed
Augmented Normalizing Flows: Bridging the Gap Between Generative Flows and Latent Variable Models

Feb 17, 2020
Chin-Wei Huang, Laurent Dinh, Aaron Courville

* 27 pages, 12 figures 

  Access Paper or Ask Questions

Discrete Flows: Invertible Generative Models of Discrete Data

May 24, 2019
Dustin Tran, Keyon Vafa, Kumar Krishna Agrawal, Laurent Dinh, Ben Poole


  Access Paper or Ask Questions

A RAD approach to deep mixture models

Mar 18, 2019
Laurent Dinh, Jascha Sohl-Dickstein, Razvan Pascanu, Hugo Larochelle

* 9 pages of main content, 4 pages of appendices 

  Access Paper or Ask Questions

VideoFlow: A Flow-Based Generative Model for Video

Mar 04, 2019
Manoj Kumar, Mohammad Babaeizadeh, Dumitru Erhan, Chelsea Finn, Sergey Levine, Laurent Dinh, Durk Kingma


  Access Paper or Ask Questions

Learning Awareness Models

Apr 17, 2018
Brandon Amos, Laurent Dinh, Serkan Cabi, Thomas Rothörl, Sergio Gómez Colmenarejo, Alistair Muldal, Tom Erez, Yuval Tassa, Nando de Freitas, Misha Denil

* Accepted to ICLR 2018 

  Access Paper or Ask Questions

Learnable Explicit Density for Continuous Latent Space and Variational Inference

Oct 06, 2017
Chin-Wei Huang, Ahmed Touati, Laurent Dinh, Michal Drozdzal, Mohammad Havaei, Laurent Charlin, Aaron Courville

* 2 figures, 5 pages, submitted to ICML Principled Approaches to Deep Learning workshop 

  Access Paper or Ask Questions

Sharp Minima Can Generalize For Deep Nets

May 15, 2017
Laurent Dinh, Razvan Pascanu, Samy Bengio, Yoshua Bengio

* 8.5 pages of main content, 2.5 of bibliography and 1 page of appendix 

  Access Paper or Ask Questions

Density estimation using Real NVP

Feb 27, 2017
Laurent Dinh, Jascha Sohl-Dickstein, Samy Bengio

* 10 pages of main content, 3 pages of bibliography, 18 pages of appendix. Accepted at ICLR 2017 

  Access Paper or Ask Questions

Theano: A Python framework for fast computation of mathematical expressions

May 09, 2016
The Theano Development Team, Rami Al-Rfou, Guillaume Alain, Amjad Almahairi, Christof Angermueller, Dzmitry Bahdanau, Nicolas Ballas, Frédéric Bastien, Justin Bayer, Anatoly Belikov, Alexander Belopolsky, Yoshua Bengio, Arnaud Bergeron, James Bergstra, Valentin Bisson, Josh Bleecher Snyder, Nicolas Bouchard, Nicolas Boulanger-Lewandowski, Xavier Bouthillier, Alexandre de Brébisson, Olivier Breuleux, Pierre-Luc Carrier, Kyunghyun Cho, Jan Chorowski, Paul Christiano, Tim Cooijmans, Marc-Alexandre Côté, Myriam Côté, Aaron Courville, Yann N. Dauphin, Olivier Delalleau, Julien Demouth, Guillaume Desjardins, Sander Dieleman, Laurent Dinh, Mélanie Ducoffe, Vincent Dumoulin, Samira Ebrahimi Kahou, Dumitru Erhan, Ziye Fan, Orhan Firat, Mathieu Germain, Xavier Glorot, Ian Goodfellow, Matt Graham, Caglar Gulcehre, Philippe Hamel, Iban Harlouchet, Jean-Philippe Heng, Balázs Hidasi, Sina Honari, Arjun Jain, Sébastien Jean, Kai Jia, Mikhail Korobov, Vivek Kulkarni, Alex Lamb, Pascal Lamblin, Eric Larsen, César Laurent, Sean Lee, Simon Lefrancois, Simon Lemieux, Nicholas Léonard, Zhouhan Lin, Jesse A. Livezey, Cory Lorenz, Jeremiah Lowin, Qianli Ma, Pierre-Antoine Manzagol, Olivier Mastropietro, Robert T. McGibbon, Roland Memisevic, Bart van Merriënboer, Vincent Michalski, Mehdi Mirza, Alberto Orlandi, Christopher Pal, Razvan Pascanu, Mohammad Pezeshki, Colin Raffel, Daniel Renshaw, Matthew Rocklin, Adriana Romero, Markus Roth, Peter Sadowski, John Salvatier, François Savard, Jan Schlüter, John Schulman, Gabriel Schwartz, Iulian Vlad Serban, Dmitriy Serdyuk, Samira Shabanian, Étienne Simon, Sigurd Spieckermann, S. Ramana Subramanyam, Jakub Sygnowski, Jérémie Tanguay, Gijs van Tulder, Joseph Turian, Sebastian Urban, Pascal Vincent, Francesco Visin, Harm de Vries, David Warde-Farley, Dustin J. Webb, Matthew Willson, Kelvin Xu, Lijun Xue, Li Yao, Saizheng Zhang, Ying Zhang

* 19 pages, 5 figures 

  Access Paper or Ask Questions

A Recurrent Latent Variable Model for Sequential Data

Apr 06, 2016
Junyoung Chung, Kyle Kastner, Laurent Dinh, Kratarth Goel, Aaron Courville, Yoshua Bengio


  Access Paper or Ask Questions

NICE: Non-linear Independent Components Estimation

Apr 10, 2015
Laurent Dinh, David Krueger, Yoshua Bengio

* 11 pages and 2 pages Appendix, workshop paper at ICLR 2015 

  Access Paper or Ask Questions

Techniques for Learning Binary Stochastic Feedforward Neural Networks

Apr 09, 2015
Tapani Raiko, Mathias Berglund, Guillaume Alain, Laurent Dinh


  Access Paper or Ask Questions

Predicting Parameters in Deep Learning

Oct 27, 2014
Misha Denil, Babak Shakibi, Laurent Dinh, Marc'Aurelio Ranzato, Nando de Freitas


  Access Paper or Ask Questions