Steerable Playlist Generation by Learning Song Similarity from Radio Station Playlists. - François Maillet; Douglas Eck; Guillaume Desjardins; Paul Lamere (ISMIR, 2009)

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)

Adaptive Parallel Tempering for Stochastic Maximum Likelihood Learning of RBMs - Guillaume Desjardins; Aaron C. Courville; Yoshua Bengio (CoRR, 2010)

Tempered Markov Chain Monte Carlo for training of Restricted Boltzmann Machines. - Guillaume Desjardins; Aaron C. Courville; Yoshua Bengio; Pascal Vincent; Olivier Delalleau (AISTATS, 2010)

On Tracking The Partition Function. - Guillaume Desjardins; Aaron C. Courville; Yoshua Bengio (NIPS, 2011)

On Training Deep Boltzmann Machines - Guillaume Desjardins; Aaron C. Courville; Yoshua Bengio (CoRR, 2012)

Disentangling Factors of Variation via Generative Entangling - Guillaume Desjardins; Aaron C. Courville; 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)

Metric-Free Natural Gradient for Joint-Training of Boltzmann Machines - Guillaume Desjardins; Razvan Pascanu; Aaron C. Courville; Yoshua Bengio (ICLR (Poster), 2013)

Deep Tempering. - Guillaume Desjardins; Heng Luo; Aaron C. Courville; Yoshua Bengio (CoRR, 2014)

The Spike-and-Slab RBM and Extensions to Discrete and Sparse Data Distributions. - Aaron C. Courville; Guillaume Desjardins; James Bergstra; Yoshua Bengio (IEEE Trans. Pattern Anal. Mach. Intell., 2014)

Compress and Control. - Joel Veness; Marc G. Bellemare; Marcus Hutter; Alvin Chua; Guillaume Desjardins (AAAI, 2015)

Natural Neural Networks. - Guillaume Desjardins; Karen Simonyan; Razvan Pascanu; Koray Kavukcuoglu (NIPS, 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)

Overcoming catastrophic forgetting in neural networks. - James Kirkpatrick; Razvan Pascanu; Neil C. Rabinowitz; Joel Veness; Guillaume Desjardins; Andrei A. Rusu; Kieran Milan; John Quan; Tiago Ramalho; Agnieszka Grabska-Barwinska; Demis Hassabis; Claudia Clopath; et al. (CoRR, 2016)

Policy Distillation. - Andrei A. Rusu; Sergio Gomez Colmenarejo; Çaglar Gülçehre; Guillaume Desjardins; James Kirkpatrick; Razvan Pascanu; Volodymyr Mnih; Koray Kavukcuoglu; Raia Hadsell (ICLR (Poster), 2016)

Progressive Neural Networks. - Andrei A. Rusu; Neil C. Rabinowitz; Guillaume Desjardins; Hubert Soyer; James Kirkpatrick; Koray Kavukcuoglu; Razvan Pascanu; Raia Hadsell (CoRR, 2016)

Understanding disentangling in β-VAE. - Christopher P. Burgess; Irina Higgins; Arka Pal; Loïc Matthey; Nick Watters; Guillaume Desjardins; Alexander Lerchner (CoRR, 2018)

Information asymmetry in KL-regularized RL. - Alexandre Galashov; Siddhant M. Jayakumar; Leonard Hasenclever; Dhruva Tirumala; Jonathan Schwarz; Guillaume Desjardins; Wojciech M. Czarnecki; Yee Whye Teh; Razvan Pascanu; Nicolas Heess (ICLR (Poster), 2019)

Self-regulation Strategies of Students Enrolled in a Distance and Online University Education Program. - Louise Sauvé; Nicole Racette; Cathia Papi; Serge Gérin-Lajoie; Guillaume Desjardins; Sophie Marineau (ICWL, 2019)

Importance Weighted Policy Learning and Adaption. - Alexandre Galashov; Jakub Sygnowski; Guillaume Desjardins; Jan Humplik; Leonard Hasenclever; Rae Jeong; Yee Whye Teh; Nicolas Heess (CoRR, 2020)

Associations of Student Characteristics and Course Organization Factors with Dropping out of University Distance and Online Learning. - Louise Sauvé; Cathia Papi; Serge Gérin-Lajoie; Guillaume Desjardins (CSEDU (1), 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)

Reward is enough for convex MDPs. - Tom Zahavy; Brendan O'Donoghue; Guillaume Desjardins; Satinder Singh 001 (NeurIPS, 2021)

Rapid training of deep neural networks without skip connections or normalization layers using Deep Kernel Shaping. - James Martens; Andy Ballard; Guillaume Desjardins; Grzegorz Swirszcz; Valentin Dalibard; Jascha Sohl-Dickstein; Samuel S. Schoenholz (CoRR, 2021)

Using Inclusive Design for People with Cognitive Limitations to Develop Online Training in the Workplace. - Louise Sauvé; Patrick Plante; Gustavo Adolfo Angulo Mendoza; Caroline Brassard; Guillaume Desjardins (CSEDU (1), 2022)

Behavior Priors for Efficient Reinforcement Learning. - Dhruva Tirumala; Alexandre Galashov; Hyeonwoo Noh; Leonard Hasenclever; Razvan Pascanu; Jonathan Schwarz; Guillaume Desjardins; Wojciech Marian Czarnecki; Arun Ahuja; Yee Whye Teh; Nicolas Heess (J. Mach. Learn. Res., 2022)

APART: Diverse Skill Discovery using All Pairs with Ascending Reward and DropouT. - Hadar Schreiber Galler; Tom Zahavy; Guillaume Desjardins; Alon Cohen (CoRR, 2023)

Developing the Digital Literacy of People with Cognitive Limitations in the Workplace. - Louise Sauvé; Patrick Plante; Gustavo Adolfo Angulo Mendoza; Caroline Brassard; Guillaume Desjardins (SN Comput. Sci., 2023)

Imitating Language via Scalable Inverse Reinforcement Learning. - Markus Wulfmeier; Michael Bloesch; Nino Vieillard; Arun Ahuja; Jörg Bornschein; Sandy H. Huang; Artem Sokolov; Matt Barnes 001; Guillaume Desjardins; Alex Bewley; Sarah Bechtle; Jost Tobias Springenberg; et al. (NeurIPS, 2024)

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models. - Soham De; Samuel L. Smith; Anushan Fernando; Aleksandar Botev; George-Cristian Muraru; Albert Gu; Ruba Haroun; Leonard Berrada; Yutian Chen 001; Srivatsan Srinivasan; Guillaume Desjardins; Arnaud Doucet; et al. (CoRR, 2024)

RecurrentGemma: Moving Past Transformers for Efficient Open Language Models. - Aleksandar Botev; Soham De; Samuel L. Smith; Anushan Fernando; George-Cristian Muraru; Ruba Haroun; Leonard Berrada; Razvan Pascanu; Pier Giuseppe Sessa; Robert Dadashi; Léonard Hussenot; Johan Ferret; et al. (CoRR, 2024)