Portrait of Hugo Larochelle

Hugo Larochelle

Core Industry Member
Adjunct professor, Université de Montréal, Depatment of Computer Science and Operations Research
Research Scientist
Research Topics
Deep Learning
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Biography

I am a researcher and adjunct professor at Université de Montréal. My research focuses on the study and development of deep learning algorithms.

Current Students

Sangnie Bhardwaj
PhD - Université de Montréal
Principal supervisor :
Guillaume Lajoie
Mélisande Astrid Crystal Teng
PhD - Université de Montréal
Principal supervisor :
Yoshua Bengio
Website
Github
Hattie Zhou
PhD - Université de Montréal
Co-supervisor :
Aaron Courville

Publications

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'alche-buc
E. Fox
Hugo Larochelle
arxiv.org
Learning a Universal Template for Few-shot Dataset Generalization
Eleni Triantafillou
Hugo Larochelle
Richard Zemel
Vincent Dumoulin
2021-01-01
ICML (published)
proceedings.mlr.press
arxiv.org
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
Meta and transfer learning are two successful families of approaches to few-shot 1 learning. Despite highly related goals, state-of-the-art … (see more)advances in each family are 2 measured largely in isolation of each other. As a result of diverging evaluation 3 norms, a direct or thorough comparison of different approaches is challenging. 4 To bridge this gap, we introduce a few-shot classification evaluation protocol 5 named VTAB+MD with the explicit goal of facilitating sharing of insights from 6 each community. We demonstrate its accessibility in practice by performing a 7 cross-family study of the best transfer and meta learners which report on both a 8 large-scale meta-learning benchmark (Meta-Dataset, MD), and a transfer learning 9 benchmark (Visual Task Adaptation Benchmark, VTAB). We find that, on average, 10 large-scale transfer methods (Big Transfer, BiT) outperform competing approaches 11 on MD, even when trained only on ImageNet. In contrast, meta-learning approaches 12 struggle to compete on VTAB when trained and validated on MD. However, BiT 13 is not without limitations, and pushing for scale does not improve performance 14 on highly out-of-distribution MD tasks. We hope that this work contributes to 15 accelerating progress on few-shot learning research. 16
2021-01-01
NeurIPS Datasets and Benchmarks (published)
openreview.net
openreview.net
A Universal Representation Transformer Layer for Few-Shot Image Classification
Lu Liu
William L. Hamilton
Guodong Long
Jing Jiang
Hugo Larochelle
Few-shot classification aims to recognize unseen classes when presented with only a small number of samples. We consider the problem of mult… (see more)i-domain few-shot image classification, where unseen classes and examples come from diverse data sources. This problem has seen growing interest and has inspired the development of benchmarks such as Meta-Dataset. A key challenge in this multi-domain setting is to effectively integrate the feature representations from the diverse set of training domains. Here, we propose a Universal Representation Transformer (URT) layer, that meta-learns to leverage universal features for few-shot classification by dynamically re-weighting and composing the most appropriate domain-specific representations. In experiments, we show that URT sets a new state-of-the-art result on Meta-Dataset. Specifically, it achieves top-performance on the highest number of data sources compared to competing methods. We analyze variants of URT and present a visualization of the attention score heatmaps that sheds light on how the model performs cross-domain generalization.
2021-01-01
ICLR (published)
openreview.net
openreview.net
Revisiting Fundamentals of Experience Replay
William Fedus
Prajit Ramachandran
Rishabh Agarwal
Yoshua Bengio
Hugo Larochelle
Mark Rowland
Will Dabney
Experience replay is central to off-policy algorithms in deep reinforcement learning (RL), but there remain significant gaps in our understa… (see more)nding. We therefore present a systematic and extensive analysis of experience replay in Q-learning methods, focusing on two fundamental properties: the replay capacity and the ratio of learning updates to experience collected (replay ratio). Our additive and ablative studies upend conventional wisdom around experience replay -- greater capacity is found to substantially increase the performance of certain algorithms, while leaving others unaffected. Counterintuitively we show that theoretically ungrounded, uncorrected n-step returns are uniquely beneficial while other techniques confer limited benefit for sifting through larger memory. Separately, by directly controlling the replay ratio we contextualize previous observations in the literature and empirically measure its importance across a variety of deep RL algorithms. Finally, we conclude by testing a set of hypotheses on the nature of these performance benefits.
2020-11-21
Proceedings of the 37th International Conference on Machine Learning (published)
proceedings.mlr.press
arxiv.org
An Effective Anti-Aliasing Approach for Residual Networks
Cristina Vasconcelos
Hugo Larochelle
Vincent Dumoulin
Nicolas Le Roux
Ross Goroshin
Image pre-processing in the frequency domain has traditionally played a vital role in computer vision and was even part of the standard pipe… (see more)line in the early days of deep learning. However, with the advent of large datasets, many practitioners concluded that this was unnecessary due to the belief that these priors can be learned from the data itself. Frequency aliasing is a phenomenon that may occur when sub-sampling any signal, such as an image or feature map, causing distortion in the sub-sampled output. We show that we can mitigate this effect by placing non-trainable blur filters and using smooth activation functions at key locations, particularly where networks lack the capacity to learn them. These simple architectural changes lead to substantial improvements in out-of-distribution generalization on both image classification under natural corruptions on ImageNet-C [10] and few-shot learning on Meta-Dataset [17], without introducing additional trainable parameters and using the default hyper-parameters of open source codebases.
2020-11-20
ArXiv (preprint)
arxiv.org
arxiv.org
On Catastrophic Interference in Atari 2600 Games
William Fedus
Dibya Ghosh
John D. Martin
Marc Gendron-Bellemare
Yoshua Bengio
Hugo Larochelle
Model-free deep reinforcement learning is sample inefficient. One hypothesis -- speculated, but not confirmed -- is that catastrophic interf… (see more)erence within an environment inhibits learning. We test this hypothesis through a large-scale empirical study in the Arcade Learning Environment (ALE) and, indeed, find supporting evidence. We show that interference causes performance to plateau; the network cannot train on segments beyond the plateau without degrading the policy used to reach there. By synthetically controlling for interference, we demonstrate performance boosts across architectures, learning algorithms and environments. A more refined analysis shows that learning one segment of a game often increases prediction errors elsewhere. Our study provides a clear empirical link between catastrophic interference and sample efficiency in reinforcement learning.
2020-02-28
ArXiv (preprint)
arxiv.org
arxiv.org
Language GANs Falling Short
Massimo Caccia
Lucas Caccia
William Fedus
Hugo Larochelle
Joelle Pineau
Laurent Charlin
Generating high-quality text with sufficient diversity is essential for a wide range of Natural Language Generation (NLG) tasks. Maximum-Lik… (see more)elihood (MLE) models trained with teacher forcing have consistently been reported as weak baselines, where poor performance is attributed to exposure bias (Bengio et al., 2015; Ranzato et al., 2015); at inference time, the model is fed its own prediction instead of a ground-truth token, which can lead to accumulating errors and poor samples. This line of reasoning has led to an outbreak of adversarial based approaches for NLG, on the account that GANs do not suffer from exposure bias. In this work, we make several surprising observations which contradict common beliefs. First, we revisit the canonical evaluation framework for NLG, and point out fundamental flaws with quality-only evaluation: we show that one can outperform such metrics using a simple, well-known temperature parameter to artificially reduce the entropy of the model's conditional distributions. Second, we leverage the control over the quality / diversity trade-off given by this parameter to evaluate models over the whole quality-diversity spectrum and find MLE models constantly outperform the proposed GAN variants over the whole quality-diversity space. Our results have several implications: 1) The impact of exposure bias on sample quality is less severe than previously thought, 2) temperature tuning provides a better quality / diversity trade-off than adversarial training while being easier to train, easier to cross-validate, and less computationally expensive. Code to reproduce the experiments is available at github.com/pclucas14/GansFallingShort
2020-01-01
ICLR (published)
openreview.net
openreview.net
Learning Graph Structure With A Finite-State Automaton Layer
Daniel D. Johnson
Hugo Larochelle
Daniel Tarlow
arxiv.org
Small-GAN: Speeding Up GAN Training Using Core-sets
Samarth Sinha
Han Zhang
Anirudh Goyal
Yoshua Bengio
Hugo Larochelle
Augustus Odena
Recent work by Brock et al. (2018) suggests that Generative Adversarial Networks (GANs) benefit disproportionately from large mini-batch siz… (see more)es. Unfortunately, using large batches is slow and expensive on conventional hardware. Thus, it would be nice if we could generate batches that were effectively large though actually small. In this work, we propose a method to do this, inspired by the use of Coreset-selection in active learning. When training a GAN, we draw a large batch of samples from the prior and then compress that batch using Coreset-selection. To create effectively large batches of 'real' images, we create a cached dataset of Inception activations of each training image, randomly project them down to a smaller dimension, and then use Coreset-selection on those projected activations at training time. We conduct experiments showing that this technique substantially reduces training time and memory usage for modern GAN variants, that it reduces the fraction of dropped modes in a synthetic dataset, and that it allows GANs to reach a new state of the art in anomaly detection.
2020-01-01
ICML (published)
proceedings.mlr.press
openreview.net
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
We show that the sum of the implicit generator log-density …
arxiv.org
InfoBot: Structured Exploration in ReinforcementLearning Using Information Bottleneck
Anirudh Goyal
Riashat Islam
D. Strouse
Zafarali Ahmed
Matthew Botvinick
Hugo Larochelle
Yoshua Bengio
Sergey Levine