Theano: A CPU and GPU Math Compiler in Python. - James Bergstra; Olivier Breuleux; Frédéric Bastien; Pascal Lamblin; Razvan Pascanu; Guillaume Desjardins; Joseph P. Turian; David Warde-Farley; Yoshua Bengio (SciPy, 2010)

The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. - David Warde-Farley; Sylva L. Donaldson; Ovi Comes; Khalid Zuberi; Rashad Badrawi; Pauline Chao; Max Franz; Chris Grouios; Farzana Kazi; Christian Tannus Lopes; Anson Maitland; Sara Mostafavi; et al. (Nucleic Acids Res., 2010)

Mixture Model for Sub-Phenotyping in GWAS. - David Warde-Farley; Michael Brudno; Quaid Morris; Anna Goldenberg (Pacific Symposium on Biocomputing, 2012)

Theano: new features and speed improvements - Frédéric Bastien; Pascal Lamblin; Razvan Pascanu; James Bergstra; Ian J. Goodfellow; Arnaud Bergeron; Nicolas Bouchard; David Warde-Farley; Yoshua Bengio (CoRR, 2012)

Unsupervised and Transfer Learning Challenge: a Deep Learning Approach. - Grégoire Mesnil; Yann N. Dauphin; Xavier Glorot; Salah Rifai; Yoshua Bengio; Ian J. Goodfellow; Erick Lavoie; Xavier Muller; Guillaume Desjardins; David Warde-Farley; Pascal Vincent; Aaron C. Courville; et al. (ICML Unsupervised and Transfer Learning, 2012)

Combining modality specific deep neural networks for emotion recognition in video. - Samira Ebrahimi Kahou; Christopher J. Pal; Xavier Bouthillier; Pierre Froumenty; Çaglar Gülçehre; Roland Memisevic; Pascal Vincent; Aaron C. Courville; Yoshua Bengio; Raul Chandias Ferrari; Mehdi Mirza; Sébastien Jean; et al. (ICMI, 2013)

Maxout Networks. - Ian J. Goodfellow; David Warde-Farley; Mehdi Mirza; Aaron C. Courville; Yoshua Bengio (ICML (3), 2013)

Pylearn2: a machine learning research library. - Ian J. Goodfellow; David Warde-Farley; Pascal Lamblin; Vincent Dumoulin; Mehdi Mirza; Razvan Pascanu; James Bergstra; Frédéric Bastien; Yoshua Bengio (CoRR, 2013)

Generative Adversarial Nets. - Ian J. Goodfellow; Jean Pouget-Abadie; Mehdi Mirza; Bing Xu; David Warde-Farley; Sherjil Ozair; Aaron C. Courville; Yoshua Bengio (NIPS, 2014)

An empirical analysis of dropout in piecewise linear networks. - David Warde-Farley; Ian J. Goodfellow; Aaron C. Courville; Yoshua Bengio (ICLR (Poster), 2014)

Blocks and Fuel: Frameworks for deep learning. - Bart van Merriënboer; Dzmitry Bahdanau; Vincent Dumoulin; Dmitriy Serdyuk; David Warde-Farley; Jan Chorowski; Yoshua Bengio (CoRR, 2015)

Self-informed neural network structure learning. - David Warde-Farley; Andrew Rabinovich; Dragomir Anguelov (ICLR (Workshop), 2015)

Theano: A Python framework for fast computation of mathematical expressions. - Rami Al-Rfou; Guillaume Alain; Amjad Almahairi; Christof Angermüller; Dzmitry Bahdanau; Nicolas Ballas; Frédéric Bastien; Justin Bayer; Anatoly Belikov; Alexander Belopolsky; Yoshua Bengio; Arnaud Bergeron; et al. (CoRR, 2016)

EmoNets: Multimodal deep learning approaches for emotion recognition in video. - Samira Ebrahimi Kahou; Xavier Bouthillier; Pascal Lamblin; Çaglar Gülçehre; Vincent Michalski; Kishore Konda; Sébastien Jean; Pierre Froumenty; Yann N. Dauphin; Nicolas Boulanger-Lewandowski; Raul Chandias Ferrari; Mehdi Mirza; et al. (J. Multimodal User Interfaces, 2016)

Improving Generative Adversarial Networks with Denoising Feature Matching. - David Warde-Farley; Yoshua Bengio (ICLR (Poster), 2017)

Variational Approaches for Auto-Encoding Generative Adversarial Networks. - Mihaela Rosca; Balaji Lakshminarayanan; David Warde-Farley; Shakir Mohamed (CoRR, 2017)

Brain tumor segmentation with Deep Neural Networks. - Mohammad Havaei; Axel Davy; David Warde-Farley; Antoine Biard; Aaron C. Courville; Yoshua Bengio; Chris Pal; Pierre-Marc Jodoin; Hugo Larochelle (Medical Image Anal., 2017)

Unsupervised Control Through Non-Parametric Discriminative Rewards. - David Warde-Farley; Tom Van de Wiele; Tejas D. Kulkarni; Catalin Ionescu; Steven Hansen 001; Volodymyr Mnih (ICLR (Poster), 2019)

Fast Task Inference with Variational Intrinsic Successor Features. - Steven Hansen 001; Will Dabney; André Barreto 001; David Warde-Farley; Tom Van de Wiele; Volodymyr Mnih (ICLR, 2020)

Generative adversarial networks. - Ian J. Goodfellow; Jean Pouget-Abadie; Mehdi Mirza; Bing Xu; David Warde-Farley; Sherjil Ozair; Aaron C. Courville; Yoshua Bengio (Commun. ACM, 2020)

Q-Learning in enormous action spaces via amortized approximate maximization. - Tom Van de Wiele; David Warde-Farley; Andriy Mnih; Volodymyr Mnih (CoRR, 2020)

Relative Variational Intrinsic Control. - Kate Baumli; David Warde-Farley; Steven Hansen 001; Volodymyr Mnih (AAAI, 2021)

Entropic Desired Dynamics for Intrinsic Control. - Steven Hansen 001; Guillaume Desjardins; Kate Baumli; David Warde-Farley; Nicolas Heess; Simon Osindero; Volodymyr Mnih (NeurIPS, 2021)

Learning more skills through optimistic exploration. - DJ Strouse; Kate Baumli; David Warde-Farley; Volodymyr Mnih; Steven Stenberg Hansen (ICLR, 2022)

Solving MaxSAT with Matrix Multiplication. - David Warde-Farley; Vinod Nair; Yujia Li 001; Ivan Lobov; Felix Gimeno; Simon Osindero (CoRR, 2023)

Evaluating Model Bias Requires Characterizing its Mistakes. - Isabela Albuquerque; Jessica Schrouff; David Warde-Farley; Ali Taylan Cemgil; Sven Gowal; Olivia Wiles (ICML, 2024)

Neural Compression of Atmospheric States. - Piotr Mirowski; David Warde-Farley; Mihaela Rosca; Matthew Koichi Grimes; Yana Hasson; Hyunjik Kim; Mélanie Rey; Simon Osindero; Suman V. Ravuri; Shakir Mohamed (CoRR, 2024)