Portrait de Hugo Larochelle

Hugo Larochelle

Membre industriel principal
Professeur associé, Université de Montréal, Département d'informatique et de recherche opérationnelle
Chercheur scientifique
Directeur scientifique, Équipe de direction
Sujets de recherche
Apprentissage profond

Biographie

Hugo Larochelle est un chercheur pionnier en apprentissage profond, leader industriel et philanthrope.

Il a commencé son parcours académique auprès de deux des « Pères fondateurs » de l'intelligence artificielle : Yoshua Bengio, son directeur de thèse à l'Université de Montréal, et Geoffrey Hinton, son superviseur postdoctoral à l'Université de Toronto.

Au fil des ans, ses recherches ont mené à plusieurs découvertes majeures présentes dans les systèmes d'IA modernes. Ses travaux sur les auto-encodeurs débruiteurs (denoising autoencoders) ont identifié la reconstruction de données brutes à partir de versions corrompues comme un paradigme clé pour l'apprentissage de représentations abstraites utiles à partir de grandes quantités de données non étiquetées. Avec des modèles tels que l'estimateur de distribution autorégressif neuronal (neural autoregressive distribution estimator) et l'auto-encodeur masqué pour l'estimation de distribution (masked autoencoder distribution estimator), il a contribué à populariser la modélisation autorégressive avec des réseaux de neurones, un paradigme aujourd'hui omniprésent dans l'IA générative. Ses travaux sur l'apprentissage de nouvelles tâches sans données (Zero-Data Learning of New Tasks) ont introduit pour la première fois le concept aujourd'hui courant d'apprentissage zero-shot.

Il a ensuite transposé son expertise académique à l'industrie en cofondant la startup Whetlab, qui a été rachetée par Twitter en 2015. Après avoir travaillé chez Twitter Cortex, il a été recruté pour diriger le laboratoire de recherche en IA de Google à Montréal (Google Brain), maintenant intégré à Google DeepMind. Il est professeur associé à l'Université de Montréal où il mentore la prochaine génération de chercheuses et chercheurs en IA. Il a également développé une série de cours en ligne gratuits sur l’apprentissage automatique.

Père de quatre enfants, Hugo Larochelle et sa conjointe, Angèle St-Pierre, ont également fait de multiples dons à l'Université de Montréal, à l'Université de Sherbrooke (où il a été professeur) et l’Université Laval pour soutenir les étudiantes et étudiants et faire avancer la recherche, particulièrement dans le domaine de l'IA pour l’environnement. Il a également initié la conférence TechAide, qui mobilise la communauté technologique de Montréal pour amasser des fonds pour Centraide, soutenant ainsi la mission de l'organisme de bienfaisance de lutter contre la pauvreté et l'exclusion sociale.

Étudiants actuels

Doctorat - UdeM
Superviseur⋅e principal⋅e :
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Doctorat - UdeM
Co-superviseur⋅e :

Publications

Identifying birdsong syllables without labelled data
Identifying sequences of syllables within birdsongs is key to tackling a wide array of challenges, including bird individual identification … (voir plus)and better understanding of animal communication and sensory-motor learning. Recently, machine learning approaches have demonstrated great potential to alleviate the need for experts to label long audio recordings by hand. However, they still typically rely on the availability of labelled data for model training, restricting applicability to a few species and datasets. In this work, we build the first fully unsupervised algorithm to decompose birdsong recordings into sequences of syllables. We first detect syllable events, then cluster them to extract templates -- syllable representations -- before performing matching pursuit to decompose the recording as a sequence of syllables. We evaluate our automatic annotations against human labels on a dataset of Bengalese finch songs and find that our unsupervised method achieves high performance. We also demonstrate that our approach can distinguish individual birds within a species through their unique vocal signatures, for both Bengalese finches and another species, the great tit.
Low Compute Unlearning via Sparse Representations
Frederik Träuble
Ashish Malik
Michael Curtis Mozer
Sanjeev Arora
Anirudh Goyal
Machine unlearning, which involves erasing knowledge about a \emph{forget set} from a trained model, can prove to be costly and infeasible … (voir plus)using existing techniques. We propose a low-compute unlearning technique based on a discrete representational bottleneck. We show that the proposed technique efficiently unlearns the forget set and incurs negligible damage to the model's performance on the rest of the dataset. We evaluate the proposed technique on the problem of class unlearning using four datasets: CIFAR-10, CIFAR-100, LACUNA-100 and ImageNet-1k. We compare the proposed technique to SCRUB, a state-of-the-art approach which uses knowledge distillation for unlearning. Across all four datasets, the proposed technique performs as well as, if not better than SCRUB while incurring almost no computational cost.
CISO: Species Distribution Modeling Conditioned on Incomplete Species Observations
Mélisande Teng
Robin Zbinden
Laura Pollock
Devis Tuia
Species distribution models (SDMs) are widely used to predict species'geographic distributions, serving as critical tools for ecological res… (voir plus)earch and conservation planning. Typically, SDMs relate species occurrences to environmental variables representing abiotic factors, such as temperature, precipitation, and soil properties. However, species distributions are also strongly influenced by biotic interactions with other species, which are often overlooked. While some methods partially address this limitation by incorporating biotic interactions, they often assume symmetrical pairwise relationships between species and require consistent co-occurrence data. In practice, species observations are sparse, and the availability of information about the presence or absence of other species varies significantly across locations. To address these challenges, we propose CISO, a deep learning-based method for species distribution modeling Conditioned on Incomplete Species Observations. CISO enables predictions to be conditioned on a flexible number of species observations alongside environmental variables, accommodating the variability and incompleteness of available biotic data. We demonstrate our approach using three datasets representing different species groups: sPlotOpen for plants, SatBird for birds, and a new dataset, SatButterfly, for butterflies. Our results show that including partial biotic information improves predictive performance on spatially separate test sets. When conditioned on a subset of species within the same dataset, CISO outperforms alternative methods in predicting the distribution of the remaining species. Furthermore, we show that combining observations from multiple datasets can improve performance. CISO is a promising ecological tool, capable of incorporating incomplete biotic information and identifying potential interactions between species from disparate taxa.
CISO: Species Distribution Modeling Conditioned on Incomplete Species Observations
Mélisande Teng
Robin Zbinden
Laura Pollock
Devis Tuia
Species distribution models (SDMs) are widely used to predict species'geographic distributions, serving as critical tools for ecological res… (voir plus)earch and conservation planning. Typically, SDMs relate species occurrences to environmental variables representing abiotic factors, such as temperature, precipitation, and soil properties. However, species distributions are also strongly influenced by biotic interactions with other species, which are often overlooked. While some methods partially address this limitation by incorporating biotic interactions, they often assume symmetrical pairwise relationships between species and require consistent co-occurrence data. In practice, species observations are sparse, and the availability of information about the presence or absence of other species varies significantly across locations. To address these challenges, we propose CISO, a deep learning-based method for species distribution modeling Conditioned on Incomplete Species Observations. CISO enables predictions to be conditioned on a flexible number of species observations alongside environmental variables, accommodating the variability and incompleteness of available biotic data. We demonstrate our approach using three datasets representing different species groups: sPlotOpen for plants, SatBird for birds, and a new dataset, SatButterfly, for butterflies. Our results show that including partial biotic information improves predictive performance on spatially separate test sets. When conditioned on a subset of species within the same dataset, CISO outperforms alternative methods in predicting the distribution of the remaining species. Furthermore, we show that combining observations from multiple datasets can improve performance. CISO is a promising ecological tool, capable of incorporating incomplete biotic information and identifying potential interactions between species from disparate taxa.
Towards Sustainable Investment Policies Informed by Opponent Shaping
Addressing climate change requires global coordination, yet rational economic actors often prioritize immediate gains over collective welfar… (voir plus)e, resulting in social dilemmas. InvestESG is a recently proposed multi-agent simulation that captures the dynamic interplay between investors and companies under climate risk. We provide a formal characterization of the conditions under which InvestESG exhibits an intertemporal social dilemma, deriving theoretical thresholds at which individual incentives diverge from collective welfare. Building on this, we apply Advantage Alignment, a scalable opponent shaping algorithm shown to be effective in general-sum games, to influence agent learning in InvestESG. We offer theoretical insights into why Advantage Alignment systematically favors socially beneficial equilibria by biasing learning dynamics toward cooperative outcomes. Our results demonstrate that strategically shaping the learning processes of economic agents can result in better outcomes that could inform policy mechanisms to better align market incentives with long-term sustainability goals.
Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery
Mélisande Teng
Etienne Lalibert'e
Information on trees at the individual level is crucial for monitoring forest ecosystems and planning forest management. Current monitoring … (voir plus)methods involve ground measurements, requiring extensive cost, time and labor. Advances in drone remote sensing and computer vision offer great potential for mapping individual trees from aerial imagery at broad-scale. Large pre-trained vision models, such as the Segment Anything Model (SAM), represent a particularly compelling choice given limited labeled data. In this work, we compare methods leveraging SAM for the task of automatic tree crown instance segmentation in high resolution drone imagery in three use cases: 1) boreal plantations, 2) temperate forests and 3) tropical forests. We also study the integration of elevation data into models, in the form of Digital Surface Model (DSM) information, which can readily be obtained at no additional cost from RGB drone imagery. We present BalSAM, a model leveraging SAM and DSM information, which shows potential over other methods, particularly in the context of plantations. We find that methods using SAM out-of-the-box do not outperform a custom Mask R-CNN, even with well-designed prompts. However, efficiently tuning SAM end-to-end and integrating DSM information are both promising avenues for tree crown instance segmentation models.
Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery
Mélisande Teng
Etienne Lalibert'e
Information on trees at the individual level is crucial for monitoring forest ecosystems and planning forest management. Current monitoring … (voir plus)methods involve ground measurements, requiring extensive cost, time and labor. Advances in drone remote sensing and computer vision offer great potential for mapping individual trees from aerial imagery at broad-scale. Large pre-trained vision models, such as the Segment Anything Model (SAM), represent a particularly compelling choice given limited labeled data. In this work, we compare methods leveraging SAM for the task of automatic tree crown instance segmentation in high resolution drone imagery in three use cases: 1) boreal plantations, 2) temperate forests and 3) tropical forests. We also study the integration of elevation data into models, in the form of Digital Surface Model (DSM) information, which can readily be obtained at no additional cost from RGB drone imagery. We present BalSAM, a model leveraging SAM and DSM information, which shows potential over other methods, particularly in the context of plantations. We find that methods using SAM out-of-the-box do not outperform a custom Mask R-CNN, even with well-designed prompts. However, efficiently tuning SAM end-to-end and integrating DSM information are both promising avenues for tree crown instance segmentation models.
The Search for Squawk: Agile Modeling in Bioacoustics
Vincent Dumoulin
Otilia Stretcu
Jenny Hamer
Lauren Harrell
Rob Laber
Bart van Merriënboer
Amanda Navine
Patrick Hart
Ben Williams
Timothy A. C. Lamont
Tries B. Rasak
Mars Coral Restoration Team
Sheryn Brodie
Brendan Doohan
Philip Eichinski
Paul Roe
Lin Schwarzkopf
Tom Denton
The Search for Squawk: Agile Modeling in Bioacoustics
Vincent Dumoulin
Otilia Stretcu
Jenny Hamer
Lauren Harrell
Rob Laber
Bart van Merriënboer
Amanda Navine
Patrick Hart
Ben Williams
Timothy A. C. Lamont
Tries B. Rasak
Mars Coral Restoration Team
Sheryn Brodie
Brendan Doohan
Philip Eichinski
Paul Roe
Lin Schwarzkopf
Tom Denton
Assessing SAM for Tree Crown Instance Segmentation from Drone Imagery
Mélisande Teng
Etienne Lalibert'e
Assessing SAM for Tree Crown Instance Segmentation from Drone Imagery
Mélisande Teng
Etienne Lalibert'e
Capturing Individual Human Preferences with Reward Features
Andr'e Barreto
Vincent Dumoulin
Yiran Mao
Nicolas Perez-Nieves
Bobak Shahriari
Yann Dauphin