Hugo Larochelle

Selective Unlearning via Representation Erasure Using Domain Adversarial Training ICLR 2025 Don't flatten, tokenize! Unlocking the key to SoftMoE's efficacy in deep RL ICLR 2025 Many-Shot In-Context Learning NIPS 2024 Static Prediction of Runtime Errors by Learning to Execute Programs with External Resource Descriptions ICLR 2023 SatBird: a Dataset for Bird Species Distribution Modeling using Remote Sensing and Citizen Science Data NIPS 2023 Repository-Level Prompt Generation for Large Language Models of Code ICML 2023 Fortuitous Forgetting in Connectionist Networks ICLR 2022 Matching Feature Sets for Few-Shot Image Classification CVPR 2022 Head2Toe: Utilizing Intermediate Representations for Better Transfer Learning ICML 2022 Impact of Aliasing on Generalization in Deep Convolutional Networks ICCV 2021 Improving Reproducibility in Machine Learning Research(A Report from the NeurIPS 2019 Reproducibility Program) JMLR 2021 Learning a Universal Template for Few-shot Dataset Generalization ICML 2021 Learning to Combine Per-Example Solutions for Neural Program Synthesis NIPS 2021 DIBS: Diversity Inducing Information Bottleneck in Model Ensembles AAAI 2021 A Universal Representation Transformer Layer for Few-Shot Image Classification ICLR 2021 Algorithmic Improvements for Deep Reinforcement Learning Applied to Interactive Fiction AAAI 2020 Small-GAN: Speeding up GAN Training using Core-Sets ICML 2020 Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks NIPS 2020 Learning Graph Structure With A Finite-State Automaton Layer NIPS 2020 Revisiting Fundamentals of Experience Replay ICML 2020 Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples ICLR 2020 Language GANs Falling Short ICLR 2020 Curriculum By Smoothing NIPS 2020 Your GAN is Secretly an Energy-based Model and You Should Use Discriminator Driven Latent Sampling NIPS 2020 InfoBot: Transfer and Exploration via the Information Bottleneck ICLR 2019 Recall Traces: Backtracking Models for Efficient Reinforcement Learning ICLR 2019 Meta-Learning for Semi-Supervised Few-Shot Classification ICLR 2018 A Meta-Learning Perspective on Cold-Start Recommendations for Items NIPS 2017 GuessWhat?! Visual Object Discovery Through Multi-Modal Dialogue CVPR 2017 Modulating early visual processing by language NIPS 2017 Document Neural Autoregressive Distribution Estimation JMLR 2017 Neural Autoregressive Distribution Estimation JMLR 2016 Dynamic Capacity Networks ICML 2016 Domain-Adversarial Training of Neural Networks JMLR 2016 Autoencoding beyond pixels using a learned similarity metric ICML 2016 Using a Recursive Neural Network to Learn an Agent's Decision Model for Plan Recognition IJCAI 2015 Describing Videos by Exploiting Temporal Structure ICCV 2015 MADE: Masked Autoencoder for Distribution Estimation ICML 2015 An Autoencoder Approach to Learning Bilingual Word Representations NIPS 2014 Topic Modeling of Multimodal Data: An Autoregressive Approach CVPR 2014 A Deep and Tractable Density Estimator ICML 2014 Agnostic Bayesian Learning of Ensembles ICML 2014 RNADE: The real-valued neural autoregressive density-estimator NIPS 2013 A Neural Autoregressive Topic Model NIPS 2012 Practical Bayesian Optimization of Machine Learning Algorithms NIPS 2012 On Nonparametric Guidance for Learning Autoencoder Representations AISTATS 2012 Learning Algorithms for the Classification Restricted Boltzmann Machine JMLR 2012 Nonparametric Guidance of Autoencoder Representations using Label Information JMLR 2012 The Neural Autoregressive Distribution Estimator AISTATS 2011 Efficient Learning of Deep Boltzmann Machines AISTATS 2010 Learning to combine foveal glimpses with a third-order Boltzmann machine NIPS 2010 Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion JMLR 2010 Exploring Strategies for Training Deep Neural Networks JMLR 2009 Greedy Layer-Wise Training of Deep Networks NIPS 2006