Non-Local Manifold Parzen Windows. - Yoshua Bengio; Hugo Larochelle; Pascal Vincent (NIPS, 2005)

Greedy Layer-Wise Training of Deep Networks. - Yoshua Bengio; Pascal Lamblin; Dan Popovici; Hugo Larochelle (NIPS, 2006)

Nonlocal Estimation of Manifold Structure. - Yoshua Bengio; Martin Monperrus; Hugo Larochelle (Neural Comput., 2006)

An empirical evaluation of deep architectures on problems with many factors of variation. - Hugo Larochelle; Dumitru Erhan; Aaron C. Courville; James Bergstra; Yoshua Bengio (ICML, 2007)

Zero-data Learning of New Tasks. - Hugo Larochelle; Dumitru Erhan; Yoshua Bengio (AAAI, 2008)

Classification using discriminative restricted Boltzmann machines. - Hugo Larochelle; Yoshua Bengio (ICML, 2008)

Extracting and composing robust features with denoising autoencoders. - Pascal Vincent; Hugo Larochelle; Yoshua Bengio; Pierre-Antoine Manzagol (ICML, 2008)

Exploring Strategies for Training Deep Neural Networks. - Hugo Larochelle; Yoshua Bengio; Jérôme Louradour; Pascal Lamblin (J. Mach. Learn. Res., 2009)

Deep Learning using Robust Interdependent Codes. - Hugo Larochelle; Dumitru Erhan; Pascal Vincent (AISTATS, 2009)

Learning to combine foveal glimpses with a third-order Boltzmann machine. - Hugo Larochelle; Geoffrey E. Hinton (NIPS, 2010)

Efficient Learning of Deep Boltzmann Machines. - Ruslan Salakhutdinov; Hugo Larochelle (AISTATS, 2010)

Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion. - Pascal Vincent; Hugo Larochelle; Isabelle Lajoie; Yoshua Bengio; Pierre-Antoine Manzagol (J. Mach. Learn. Res., 2010)

Tractable Multivariate Binary Density Estimation and the Restricted Boltzmann Forest. - Hugo Larochelle; Yoshua Bengio; Joseph P. Turian (Neural Comput., 2010)

Learning attentional policies for tracking and recognition in video with deep networks. - Loris Bazzani; Nando de Freitas; Hugo Larochelle; Vittorio Murino; Jo-Anne Ting (ICML, 2011)

Classification of Sets using Restricted Boltzmann Machines. - Jérôme Louradour; Hugo Larochelle (UAI, 2011)

Conditional Restricted Boltzmann Machines for Structured Output Prediction. - Volodymyr Mnih; Hugo Larochelle; Geoffrey E. Hinton (UAI, 2011)

Autotagging music with conditional restricted Boltzmann machines - Michael I. Mandel; Razvan Pascanu; Hugo Larochelle; Yoshua Bengio (CoRR, 2011)

Loss-sensitive Training of Probabilistic Conditional Random Fields - Maksims Volkovs; Hugo Larochelle; Richard S. Zemel (CoRR, 2011)

The Neural Autoregressive Distribution Estimator. - Hugo Larochelle; Iain Murray 001 (AISTATS, 2011)

Learning to rank by aggregating expert preferences. - Maksims Volkovs; Hugo Larochelle; Richard S. Zemel (CIKM, 2012)

Training Restricted Boltzmann Machines on Word Observations. - George E. Dahl; Ryan Prescott Adams; Hugo Larochelle (ICML, 2012)

A Neural Autoregressive Topic Model. - Hugo Larochelle; Stanislas Lauly (NIPS, 2012)

Practical Bayesian Optimization of Machine Learning Algorithms. - Jasper Snoek; Hugo Larochelle; Ryan P. Adams (NIPS, 2012)

Detonation Classification from acoustic Signature with the Restricted Boltzmann Machine. - Yoshua Bengio; Nicolas Chapados; Olivier Delalleau; Hugo Larochelle; Xavier Saint-Mleux; Christian Hudon; Jérôme Louradour (Comput. Intell., 2012)

Learning Algorithms for the Classification Restricted Boltzmann Machine. - Hugo Larochelle; Michael I. Mandel; Razvan Pascanu; Yoshua Bengio (J. Mach. Learn. Res., 2012)

On Nonparametric Guidance for Learning Autoencoder Representations. - Jasper Snoek; Ryan Prescott Adams; Hugo Larochelle (AISTATS, 2012)

Nonparametric guidance of autoencoder representations using label information. - Jasper Snoek; Ryan P. Adams; Hugo Larochelle (J. Mach. Learn. Res., 2012)

Learning Where to Attend with Deep Architectures for Image Tracking. - Misha Denil; Loris Bazzani; Hugo Larochelle; Nando de Freitas (Neural Comput., 2012)

RNADE: The real-valued neural autoregressive density-estimator. - Benigno Uria; Iain Murray 001; Hugo Larochelle (NIPS, 2013)

NADE: The real-valued neural autoregressive density-estimator. - Benigno Uria; Iain Murray 001; Hugo Larochelle (CoRR, 2013)

A Supervised Neural Autoregressive Topic Model for Simultaneous Image Classification and Annotation - Yin Zheng; Yu-Jin Zhang; Hugo Larochelle (CoRR, 2013)

Guest Editors' Introduction: Special Section on Learning Deep Architectures. - Samy Bengio; Li Deng 001; Hugo Larochelle; Honglak Lee; Ruslan Salakhutdinov (IEEE Trans. Pattern Anal. Mach. Intell., 2013)

Topic Modeling of Multimodal Data: An Autoregressive Approach. - Yin Zheng; Yu-Jin Zhang; Hugo Larochelle (CVPR, 2014)

Agnostic Bayesian Learning of Ensembles. - Alexandre Lacoste; Mario Marchand; François Laviolette; Hugo Larochelle (ICML, 2014)

A Deep and Tractable Density Estimator. - Benigno Uria; Iain Murray 001; Hugo Larochelle (ICML, 2014)

Efficient Interactive Brain Tumor Segmentation as Within-Brain kNN Classification. - Mohammad Havaei; Pierre-Marc Jodoin; Hugo Larochelle (ICPR, 2014)

Leveraging user libraries to bootstrap collaborative filtering. - Laurent Charlin; Richard S. Zemel; Hugo Larochelle (KDD, 2014)

An Autoencoder Approach to Learning Bilingual Word Representations. - A. P. Sarath Chandar; Stanislas Lauly; Hugo Larochelle; Mitesh M. Khapra; Balaraman Ravindran; Vikas C. Raykar; Amrita Saha (NIPS, 2014)

Sequential Model-Based Ensemble Optimization. - Alexandre Lacoste; Hugo Larochelle; Mario Marchand; François Laviolette (UAI, 2014)

Domain-Adversarial Neural Networks. - Hana Ajakan; Pascal Germain; Hugo Larochelle; François Laviolette; Mario Marchand (CoRR, 2014)

Learning Multilingual Word Representations using a Bag-of-Words Autoencoder. - Stanislas Lauly; Alex Boulanger; Hugo Larochelle (CoRR, 2014)

Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality. - Alexandre Allauzen; Edward Grefenstette; Karl Moritz Hermann; Hugo Larochelle; Scott Wen-tau Yih (CVSC, 2015)

Describing Videos by Exploiting Temporal Structure. - Li Yao; Atousa Torabi; Kyunghyun Cho; Nicolas Ballas; Christopher J. Pal; Hugo Larochelle; Aaron C. Courville (ICCV, 2015)

MADE: Masked Autoencoder for Distribution Estimation. - Mathieu Germain; Karol Gregor; Iain Murray 001; Hugo Larochelle (ICML, 2015)

Using a Recursive Neural Network to Learn an Agent's Decision Model for Plan Recognition. - Francis Bisson; Hugo Larochelle; Froduald Kabanza (IJCAI, 2015)

A Convolutional Neural Network Approach to Brain Tumor Segmentation. - Mohammad Havaei; Francis Dutil; Chris Pal; Hugo Larochelle; Pierre-Marc Jodoin (Brainles@MICCAI, 2015)

Using Descriptive Video Services to Create a Large Data Source for Video Annotation Research. - Atousa Torabi; Christopher J. Pal; Hugo Larochelle; Aaron C. Courville (CoRR, 2015)

Video Description Generation Incorporating Spatio-Temporal Features and a Soft-Attention Mechanism. - Li Yao; Atousa Torabi; Kyunghyun Cho; Nicolas Ballas; Christopher Joseph Pal; Hugo Larochelle; Aaron C. Courville (CoRR, 2015)

A Neural Autoregressive Approach to Attention-based Recognition. - Yin Zheng; Richard S. Zemel; Yu-Jin Zhang; Hugo Larochelle (Int. J. Comput. Vis., 2015)

Guest Editorial: Deep Learning for Multimedia Computing. - Guo-Jun Qi; Hugo Larochelle; Benoit Huet; Jiebo Luo 001; Kai Yu (IEEE Trans. Multim., 2015)

Dynamic Capacity Networks. - Amjad Almahairi; Nicolas Ballas; Tim Cooijmans; Yin Zheng; Hugo Larochelle; Aaron C. Courville (ICML, 2016)

Autoencoding beyond pixels using a learned similarity metric. - Anders Boesen Lindbo Larsen; Søren Kaae Sønderby; Hugo Larochelle; Ole Winther (ICML, 2016)

Within-brain classification for brain tumor segmentation. - Mohammad Havaei; Hugo Larochelle; Philippe Poulin; Pierre-Marc Jodoin (Int. J. Comput. Assist. Radiol. Surg., 2016)

Hierarchical Memory Networks. - Sarath Chandar; Sungjin Ahn; Hugo Larochelle; Pascal Vincent; Gerald Tesauro; Yoshua Bengio (CoRR, 2016)

Deep learning trends for focal brain pathology segmentation in MRI. - Mohammad Havaei; Nicolas Guizard; Hugo Larochelle; Pierre-Marc Jodoin (CoRR, 2016)

Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations. - David Krueger 001; Tegan Maharaj; János Kramár; Mohammad Pezeshki; Nicolas Ballas; Nan Rosemary Ke; Anirudh Goyal; Yoshua Bengio; Hugo Larochelle; Aaron C. Courville; Chris Pal (CoRR, 2016)

Domain-Adversarial Training of Neural Networks. - Yaroslav Ganin; Evgeniya Ustinova; Hana Ajakan; Pascal Germain; Hugo Larochelle; François Laviolette; Mario Marchand; Victor S. Lempitsky (J. Mach. Learn. Res., 2016)

Neural Autoregressive Distribution Estimation. - Benigno Uria; Marc-Alexandre Côté; Karol Gregor; Iain Murray 001; Hugo Larochelle (J. Mach. Learn. Res., 2016)

Correlational Neural Networks. - Sarath Chandar; Mitesh M. Khapra; Hugo Larochelle; Balaraman Ravindran (Neural Comput., 2016)

An Infinite Restricted Boltzmann Machine. - Marc-Alexandre Côté; Hugo Larochelle (Neural Comput., 2016)

A Deep and Autoregressive Approach for Topic Modeling of Multimodal Data. - Yin Zheng; Yu-Jin Zhang; Hugo Larochelle (IEEE Trans. Pattern Anal. Mach. Intell., 2016)

Deep Learning Trends for Focal Brain Pathology Segmentation in MRI. - Mohammad Havaei; Nicolas Guizard; Hugo Larochelle; Pierre-Marc Jodoin (Machine Learning for Health Informatics, 2016)

GuessWhat?! Visual Object Discovery through Multi-modal Dialogue. - Harm de Vries; Florian Strub; Sarath Chandar; Olivier Pietquin; Hugo Larochelle; Aaron C. Courville (CVPR, 2017)

Recurrent Mixture Density Network for Spatiotemporal Visual Attention. - Loris Bazzani; Hugo Larochelle; Lorenzo Torresani (ICLR (Poster), 2017)

Optimization as a Model for Few-Shot Learning. - Sachin Ravi; Hugo Larochelle (ICLR, 2017)

Learn to Track: Deep Learning for Tractography. - Philippe Poulin; Marc-Alexandre Côté; Jean-Christophe Houde; Laurent Petit; Peter F. Neher; Klaus H. Maier-Hein; Hugo Larochelle; Maxime Descoteaux (MICCAI (1), 2017)

A Meta-Learning Perspective on Cold-Start Recommendations for Items. - Manasi Vartak; Arvind Thiagarajan; Conrado Miranda; Jeshua Bratman; Hugo Larochelle (NIPS, 2017)

Modulating early visual processing by language. - Harm de Vries; Florian Strub; Jérémie Mary; Hugo Larochelle; Olivier Pietquin; Aaron C. Courville (NIPS, 2017)

Multiscale sequence modeling with a learned dictionary. - Bart van Merriënboer; Amartya Sanyal; Hugo Larochelle; Yoshua Bengio (CoRR, 2017)

Movie Description. - Anna Rohrbach; Atousa Torabi; Marcus Rohrbach; Niket Tandon; Christopher Joseph Pal; Hugo Larochelle; Aaron C. Courville; Bernt Schiele (Int. J. Comput. Vis., 2017)

Document Neural Autoregressive Distribution Estimation. - Stanislas Lauly; Yin Zheng; Alexandre Allauzen; Hugo Larochelle (J. Mach. Learn. Res., 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)

Domain-Adversarial Training of Neural Networks. - Yaroslav Ganin; Evgeniya Ustinova; Hana Ajakan; Pascal Germain; Hugo Larochelle; François Laviolette; Mario Marchand; Victor S. Lempitsky (Domain Adaptation in Computer Vision Applications, 2017)

HoME: a Household Multimodal Environment. - Simon Brodeur; Ethan Perez; Ankesh Anand; Florian Golemo; Luca Celotti; Florian Strub; Jean Rouat; Hugo Larochelle; Aaron C. Courville (ICLR (Workshop), 2018)

Meta-Learning for Batch Mode Active Learning. - Sachin Ravi; Hugo Larochelle (ICLR (Workshop), 2018)

Meta-Learning for Semi-Supervised Few-Shot Classification. - Mengye Ren; Eleni Triantafillou; Sachin Ravi; Jake Snell; Kevin Swersky; Joshua B. Tenenbaum; Hugo Larochelle; Richard S. Zemel (ICLR (Poster), 2018)

Disentangling the independently controllable factors of variation by interacting with the world. - Valentin Thomas; Emmanuel Bengio; William Fedus; Jules Pondard; Philippe Beaudoin; Hugo Larochelle; Joelle Pineau; Doina Precup; Yoshua Bengio (CoRR, 2018)

Recall Traces: Backtracking Models for Efficient Reinforcement Learning. - Anirudh Goyal; Philemon Brakel; William Fedus; Timothy P. Lillicrap; Sergey Levine; Hugo Larochelle; Yoshua Bengio (CoRR, 2018)

Blindfold Baselines for Embodied QA. - Ankesh Anand; Eugene Belilovsky; Kyle Kastner; Hugo Larochelle; Aaron C. Courville (CoRR, 2018)

Traffic Analytics With Low-Frame-Rate Videos. - Zhiming Luo; Pierre-Marc Jodoin; Song-Zhi Su; Shao-Zi Li; Hugo Larochelle (IEEE Trans. Circuits Syst. Video Technol., 2018)

A RAD approach to deep mixture models. - Laurent Dinh; Jascha Sohl-Dickstein; Razvan Pascanu; Hugo Larochelle (DGS@ICLR, 2019)

Recall Traces: Backtracking Models for Efficient Reinforcement Learning. - Anirudh Goyal; Philemon Brakel; William Fedus; Soumye Singhal; Timothy P. Lillicrap; Sergey Levine; Hugo Larochelle; Yoshua Bengio (ICLR (Poster), 2019)

InfoBot: Transfer and Exploration via the Information Bottleneck. - Anirudh Goyal; Riashat Islam; Daniel Strouse; Zafarali Ahmed; Hugo Larochelle; Matthew M. Botvinick; Yoshua Bengio; Sergey Levine (ICLR (Poster), 2019)

Hyperbolic Discounting and Learning over Multiple Horizons. - William Fedus; Carles Gelada; Yoshua Bengio; Marc G. Bellemare; Hugo Larochelle (CoRR, 2019)

Centroid Networks for Few-Shot Clustering and Unsupervised Few-Shot Classification. - Gabriel Huang; Hugo Larochelle; Simon Lacoste-Julien (CoRR, 2019)

Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples. - Eleni Triantafillou; Tyler Zhu; Vincent Dumoulin; Pascal Lamblin; Kelvin Xu; Ross Goroshin; Carles Gelada; Kevin Swersky; Pierre-Antoine Manzagol; Hugo Larochelle (CoRR, 2019)

Learning Neural Causal Models from Unknown Interventions. - Nan Rosemary Ke; Olexa Bilaniuk; Anirudh Goyal; Stefan Bauer; Hugo Larochelle; Chris Pal; Yoshua Bengio (CoRR, 2019)

Machine behaviour. - Iyad Rahwan; Manuel Cebrián; Nick Obradovich; Josh C. Bongard; Jean-François Bonnefon; Cynthia Breazeal; Jacob W. Crandall; Nicholas A. Christakis; Iain D. Couzin; Matthew O. Jackson; Nicholas R. Jennings; Ece Kamar; et al. (Nat., 2019)

Algorithmic Improvements for Deep Reinforcement Learning Applied to Interactive Fiction. - Vishal Jain; William Fedus; Hugo Larochelle; Doina Precup; Marc G. Bellemare (AAAI, 2020)

Language GANs Falling Short. - Massimo Caccia; Lucas Caccia; William Fedus; Hugo Larochelle; Joelle Pineau; Laurent Charlin (ICLR, 2020)

Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples. - Eleni Triantafillou; Tyler Zhu; Vincent Dumoulin; Pascal Lamblin; Utku Evci; Kelvin Xu; Ross Goroshin; Carles Gelada; Kevin Swersky; Pierre-Antoine Manzagol; Hugo Larochelle (ICLR, 2020)

Revisiting Fundamentals of Experience Replay. - William Fedus; Prajit Ramachandran; Rishabh Agarwal; Yoshua Bengio; Hugo Larochelle; Mark Rowland 001; Will Dabney (ICML, 2020)

Small-GAN: Speeding up GAN Training using Core-Sets. - Samarth Sinha; Han Zhang 010; Anirudh Goyal; Yoshua Bengio; Hugo Larochelle; Augustus Odena (ICML, 2020)

Learning Graph Structure With A Finite-State Automaton Layer. - Daniel D. Johnson 001; Hugo Larochelle; Daniel Tarlow (NeurIPS, 2020)

Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks. - David Bieber; Charles Sutton; Hugo Larochelle; Daniel Tarlow (NeurIPS, 2020)

Your GAN is Secretly an Energy-based Model and You Should Use Discriminator Driven Latent Sampling. - Tong Che; Ruixiang Zhang; Jascha Sohl-Dickstein; Hugo Larochelle; Liam Paull; Yuan Cao; Yoshua Bengio (NeurIPS, 2020)

Curriculum By Smoothing. - Samarth Sinha; Animesh Garg; Hugo Larochelle (NeurIPS, 2020)

The Hanabi challenge: A new frontier for AI research. - Nolan Bard; Jakob N. Foerster; Sarath Chandar; Neil Burch; Marc Lanctot; H. Francis Song; Emilio Parisotto; Vincent Dumoulin; Subhodeep Moitra; Edward Hughes 001; Iain Dunning; Shibl Mourad; et al. (Artif. Intell., 2020)

On Catastrophic Interference in Atari 2600 Games. - William Fedus; Dibya Ghosh; John D. Martin; Marc G. Bellemare; Yoshua Bengio; Hugo Larochelle (CoRR, 2020)

Curriculum By Texture. - Samarth Sinha; Animesh Garg; Hugo Larochelle (CoRR, 2020)

Learned Equivariant Rendering without Transformation Supervision. - Cinjon Resnick; Or Litany; Hugo Larochelle; Joan Bruna; Kyunghyun Cho (CoRR, 2020)

An Effective Anti-Aliasing Approach for Residual Networks. - Cristina Nader Vasconcelos; Hugo Larochelle; Vincent Dumoulin; Nicolas Le Roux; Ross Goroshin (CoRR, 2020)

DIBS: Diversity Inducing Information Bottleneck in Model Ensembles. - Samarth Sinha; Homanga Bharadhwaj; Anirudh Goyal; Hugo Larochelle; Animesh Garg; Florian Shkurti (AAAI, 2021)

Impact of Aliasing on Generalization in Deep Convolutional Networks. - Cristina Nader Vasconcelos; Hugo Larochelle; Vincent Dumoulin; Rob Romijnders; Nicolas Le Roux; Ross Goroshin (ICCV, 2021)

A Universal Representation Transformer Layer for Few-Shot Image Classification. - Lu Liu 019; William L. Hamilton; Guodong Long; Jing Jiang 002; Hugo Larochelle (ICLR, 2021)

Learning a Universal Template for Few-shot Dataset Generalization. - Eleni Triantafillou; Hugo Larochelle; Richard S. Zemel; Vincent Dumoulin (ICML, 2021)

A Unified Few-Shot Classification Benchmark to Compare Transfer and Meta Learning Approaches. - Vincent Dumoulin; Neil Houlsby; Utku Evci; Xiaohua Zhai; Ross Goroshin; Sylvain Gelly; Hugo Larochelle (NeurIPS Datasets and Benchmarks, 2021)

Learning to Combine Per-Example Solutions for Neural Program Synthesis. - Disha Shrivastava; Hugo Larochelle; Daniel Tarlow (NeurIPS, 2021)

Self-Supervised Equivariant Scene Synthesis from Video. - Cinjon Resnick; Or Litany; Cosmas Heiß; Hugo Larochelle; Joan Bruna; Kyunghyun Cho (CoRR, 2021)

Interpretable Multi-Modal Hate Speech Detection. - Prashanth Vijayaraghavan; Hugo Larochelle; Deb Roy (CoRR, 2021)

Comparing Transfer and Meta Learning Approaches on a Unified Few-Shot Classification Benchmark. - Vincent Dumoulin; Neil Houlsby; Utku Evci; Xiaohua Zhai; Ross Goroshin; Sylvain Gelly; Hugo Larochelle (CoRR, 2021)

Improving Reproducibility in Machine Learning Research(A Report from the NeurIPS 2019 Reproducibility Program). - Joelle Pineau; Philippe Vincent-Lamarre; Koustuv Sinha; Vincent Larivière; Alina Beygelzimer; Florence d'Alché-Buc; Emily B. Fox; Hugo Larochelle (J. Mach. Learn. Res., 2021)

Matching Feature Sets for Few-Shot Image Classification. - Arman Afrasiyabi; Hugo Larochelle; Jean-François Lalonde; Christian Gagné 001 (CVPR, 2022)

Uniform Priors for Data-Efficient Learning. - Samarth Sinha; Karsten Roth; Anirudh Goyal; Marzyeh Ghassemi; Zeynep Akata; Hugo Larochelle; Animesh Garg (CVPR Workshops, 2022)

Fortuitous Forgetting in Connectionist Networks. - Hattie Zhou; Ankit Vani; Hugo Larochelle; Aaron C. Courville (ICLR, 2022)

Head2Toe: Utilizing Intermediate Representations for Better Transfer Learning. - Utku Evci; Vincent Dumoulin; Hugo Larochelle; Michael C. Mozer (ICML, 2022)

Teaching Algorithmic Reasoning via In-context Learning. - Hattie Zhou; Azade Nova; Hugo Larochelle; Aaron C. Courville; Behnam Neyshabur; Hanie Sedghi (CoRR, 2022)

Static Prediction of Runtime Errors by Learning to Execute Programs with External Resource Descriptions. - David Bieber; Rishab Goel; Daniel Zheng; Hugo Larochelle; Daniel Tarlow (ICLR, 2023)

Repository-Level Prompt Generation for Large Language Models of Code. - Disha Shrivastava; Hugo Larochelle; Daniel Tarlow (ICML, 2023)

SatBird: a Dataset for Bird Species Distribution Modeling using Remote Sensing and Citizen Science Data. - Mélisande Teng; Amna Elmustafa; Benjamin Akera; Yoshua Bengio; Hager Radi Abdelwahed; Hugo Larochelle; David Rolnick (NeurIPS, 2023)

Bird Distribution Modelling using Remote Sensing and Citizen Science data. - Mélisande Teng; Amna Elmustafa; Benjamin Akera; Hugo Larochelle; David Rolnick (CoRR, 2023)

SatBird: Bird Species Distribution Modeling with Remote Sensing and Citizen Science Data. - Mélisande Teng; Amna Elmustafa; Benjamin Akera; Yoshua Bengio; Hager Radi Abdelwahed; Hugo Larochelle; David Rolnick (CoRR, 2023)

Unlearning via Sparse Representations. - Vedant Shah; Frederik Träuble; Ashish Malik; Hugo Larochelle; Michael Mozer; Sanjeev Arora; Yoshua Bengio; Anirudh Goyal (CoRR, 2023)

Neural Causal Structure Discovery from Interventions. - Nan Rosemary Ke; Olexa Bilaniuk; Anirudh Goyal; Stefan Bauer; Hugo Larochelle; Bernhard Schölkopf; Michael Curtis Mozer; Christopher Pal; Yoshua Bengio (Trans. Mach. Learn. Res., 2023)

Many-Shot In-Context Learning. - Rishabh Agarwal; Avi Singh; Lei Zhang; Bernd Bohnet; Luis Rosias; Stephanie C. Y. Chan; Biao Zhang; Ankesh Anand; Zaheer Abbas; Azade Nova; John D. Co-Reyes; Eric Chu; et al. (NeurIPS, 2024)

Consolidating Separate Degradations Model via Weights Fusion and Distillation. - Dinesh Daultani; Hugo Larochelle (WACV (Workshops), 2024)

Many-Shot In-Context Learning. - Rishabh Agarwal; Avi Singh; Lei M. Zhang; Bernd Bohnet; Stephanie Chan 003; Ankesh Anand; Zaheer Abbas; Azade Nova; John D. Co-Reyes; Eric Chu; Feryal M. P. Behbahani; Aleksandra Faust; et al. (CoRR, 2024)

Optimisation of quantitative brain diffusion-relaxation MRI acquisition protocols with physics-informed machine learning. - Álvaro Planchuelo-Gómez; Maxime Descoteaux; Hugo Larochelle; Jana Hutter; Derek K. Jones; Chantal M. W. Tax (Medical Image Anal., 2024)

A density estimation perspective on learning from pairwise human preferences. - Vincent Dumoulin; Daniel D. Johnson 001; Pablo Samuel Castro; Hugo Larochelle; Yann N. Dauphin (Trans. Mach. Learn. Res., 2024)

Selective Unlearning via Representation Erasure Using Domain Adversarial Training. - Nazanin Mohammadi Sepahvand; Eleni Triantafillou; Hugo Larochelle; Doina Precup; James J. Clark; Daniel M. Roy 001; Gintare Karolina Dziugaite (ICLR, 2025)

Don't flatten, tokenize! Unlocking the key to SoftMoE's efficacy in deep RL. - Ghada Sokar; Johan S. Obando-Ceron; Aaron C. Courville; Hugo Larochelle; Pablo Samuel Castro (ICLR, 2025)

Capturing Individual Human Preferences with Reward Features. - André Barreto 001; Vincent Dumoulin; Yiran Mao; Nicolas Perez Nieves; Bobak Shahriari; Yann N. Dauphin; Doina Precup; Hugo Larochelle (CoRR, 2025)

Assessing SAM for Tree Crown Instance Segmentation from Drone Imagery. - Mélisande Teng; Arthur Ouaknine; Etienne Laliberté; Yoshua Bengio; David Rolnick; Hugo Larochelle (CoRR, 2025)

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery. - Mélisande Teng; Arthur Ouaknine; Etienne Laliberté; Yoshua Bengio; David Rolnick; Hugo Larochelle (CoRR, 2025)

CISO: Species Distribution Modeling Conditioned on Incomplete Species Observations. - Hager Radi Abdelwahed; Mélisande Teng; Robin Zbinden; Laura Pollock; Hugo Larochelle; Devis Tuia; David Rolnick (CoRR, 2025)