Portrait of Pascal Vincent

Pascal Vincent

Core Industry Member
Associate Professor, Université de Montréal, Department of Computer Science and Operations Research
Research Scientist, Facebook AI Research (FAIR) Montréal
Research Topics
Deep Learning
Representation Learning
Google Scholar

Biography

Pascal Vincent is a research scientist in the Fundamental AI Research (FAIR) team at Meta and an adjunct professor in the Department of Computer Science and Operations Research (DIRO) at Université de Montréal.

He is also a founding member of Mila – Quebec Artificial Intelligence Institute and an associate fellow in CIFAR’s Learning in Machines & Brains program.

Vincent’s research on principles and algorithms in representation learning led him to uncover several seminal ideas that became key enablers for the successes of deep learning methods. Among his most influential contributions is the seminal paper on neural language models “A Neural Probabilistic Language Model” (Bengio et al. 2013), which laid the foundations on which all artificial neural network based language models are built.

His work on denoising autoencoders (Vincent et al. 2008, 2010) was the first to propose the pretext task of filling in artificially introduced blanks for the sake of learning useful representations in any modality, a precursor of what is today called self-supervised learning.

In another seminal paper, “A Connection Between Score Matching and Denoising Autoencoders” (Vincent 2011), he developed the “denoising score matching” principle, which is now routinely used to train diffusion-based generative models.

Vincent’s current research focuses on novel theory and algorithms for representation learning to enable robust generalization out-of-distribution.

Current Students

Reza Bayat
PhD - Université de Montréal
Principal supervisor :
Aaron Courville
Website

Publications

Compositional Risk Minimization
Divyat Mahajan
Mohammad Pezeshki
Charles Arnal
Ioannis Mitliagkas
Kartik Ahuja
Pascal Vincent
Compositional generalization is a crucial step towards developing data-efficient intelligent machines that generalize in human-like ways. In… (see more) this work, we tackle a challenging form of distribution shift, termed compositional shift, where some attribute combinations are completely absent at training but present in the test distribution. This shift tests the model's ability to generalize compositionally to novel attribute combinations in discriminative tasks. We model the data with flexible additive energy distributions, where each energy term represents an attribute, and derive a simple alternative to empirical risk minimization termed compositional risk minimization (CRM). We first train an additive energy classifier to predict the multiple attributes and then adjust this classifier to tackle compositional shifts. We provide an extensive theoretical analysis of CRM, where we show that our proposal extrapolates to special affine hulls of seen attribute combinations. Empirical evaluations on benchmark datasets confirms the improved robustness of CRM compared to other methods from the literature designed to tackle various forms of subpopulation shifts.
2025-10-05
Proceedings of the 42nd International Conference on Machine Learning (published)
doi.org
proceedings.mlr.press
MaestroMotif: Skill Design From Artificial Intelligence Feedback
Martin Klissarov
Mikael Henaff
Roberta Raileanu
Shagun Sodhani
Pascal Vincent
Amy Zhang
Pierre-Luc Bacon
Doina Precup
Marlos C. Machado
Pierluca D'Oro
Describing skills in natural language has the potential to provide an accessible way to inject human knowledge about decision-making into an… (see more) AI system. We present MaestroMotif, a method for AI-assisted skill design, which yields high-performing and adaptable agents. MaestroMotif leverages the capabilities of Large Language Models (LLMs) to effectively create and reuse skills. It first uses an LLM's feedback to automatically design rewards corresponding to each skill, starting from their natural language description. Then, it employs an LLM's code generation abilities, together with reinforcement learning, for training the skills and combining them to implement complex behaviors specified in language. We evaluate MaestroMotif using a suite of complex tasks in the NetHack Learning Environment (NLE), demonstrating that it surpasses existing approaches in both performance and usability.
2025-01-21
ICLR.cc/2025/Conference (oral)
doi.org
openreview.net
The Pitfalls of Memorization: When Memorization Hurts Generalization
Reza Bayat
Mohammad Pezeshki
Elvis Dohmatob
David Lopez-Paz
Pascal Vincent
Neural networks often learn simple explanations that fit the majority of the data while memorizing exceptions that deviate from these explan… (see more)ations.This behavior leads to poor generalization when the learned explanations rely on spurious correlations. In this work, we formalize the interplay between memorization and generalization, showing that spurious correlations would particularly lead to poor generalization when are combined with memorization. Memorization can reduce training loss to zero, leaving no incentive to learn robust, generalizable patterns. To address this, we propose memorization-aware training (MAT), which uses held-out predictions as a signal of memorization to shift a model's logits. MAT encourages learning robust patterns invariant across distributions, improving generalization under distribution shifts.
2025-01-21
ICLR.cc/2025/Conference (poster)
doi.org
openreview.net
Motif: Intrinsic Motivation From Artificial Intelligence Feedback
Martin Klissarov
Pierluca D'Oro
Shagun Sodhani
Roberta Raileanu
Pierre-Luc Bacon
Pascal Vincent
Amy Zhang
Mikael Henaff
Exploring rich environments and evaluating one's actions without prior knowledge is immensely challenging. In this paper, we propose Motif, … (see more)a general method to interface such prior knowledge from a Large Language Model (LLM) with an agent. Motif is based on the idea of grounding LLMs for decision-making without requiring them to interact with the environment: it elicits preferences from an LLM over pairs of captions to construct an intrinsic reward, which is then used to train agents with reinforcement learning. We evaluate Motif's performance and behavior on the challenging, open-ended and procedurally-generated NetHack game. Surprisingly, by only learning to maximize its intrinsic reward, Motif achieves a higher game score than an algorithm directly trained to maximize the score itself. When combining Motif's intrinsic reward with the environment reward, our method significantly outperforms existing approaches and makes progress on tasks where no advancements have ever been made without demonstrations. Finally, we show that Motif mostly generates intuitive human-aligned behaviors which can be steered easily through prompt modifications, while scaling well with the LLM size and the amount of information given in the prompt.
2024-01-15
ICLR.cc/2024/Conference (poster)
doi.org
openreview.net
PUG: Photorealistic and Semantically Controllable Synthetic Data for Representation Learning
Florian Bordes
Shashank Shekhar
Mark Ibrahim
Diane Bouchacourt
Pascal Vincent
Ari S. Morcos
Synthetic image datasets offer unmatched advantages for designing and evaluating deep neural networks: they make it possible to (i) render a… (see more)s many data samples as needed, (ii) precisely control each scene and yield granular ground truth labels (and captions), (iii) precisely control distribution shifts between training and testing to isolate variables of interest for sound experimentation.Despite such promise, the use of synthetic image data is still limited -- and often played down -- mainly due to their lack of realism. Most works therefore rely on datasets of real images, which have often been scraped from public images on the internet, and may have issues with regards to privacy, bias, and copyright, while offering little control over how objects precisely appear.In this work, we present a path to democratize the use of photorealistic synthetic data: we develop a new generation of interactive environments for representation learning research, that offer both controllability and realism. We use the Unreal Engine, a powerful game engine well known in the entertainment industry, to produce PUG (Photorealistic Unreal Graphics) environments and datasets for representation learning. Using PUG for evaluation and fine-tuning, we demonstrate the potential of PUG to both enable more rigorous evaluations and to improve model training.
2023-09-24
NeurIPS.cc/2023/Track/Datasets_and_Benchmarks (poster)
doi.org
openreview.net
Do SSL Models Have Déjà Vu? A Case of Unintended Memorization in Self-supervised Learning
Casey Meehan
Florian Bordes
Pascal Vincent
Kamalika Chaudhuri
Chuan Guo
Self-supervised learning (SSL) algorithms can produce useful image representations by learning to associate different parts of natural image… (see more)s with one another. However, when taken to the extreme, SSL models can unintendedly memorize specific parts in individual training samples rather than learning semantically meaningful associations. In this work, we perform a systematic study of the unintended memorization of image-specific information in SSL models -- which we refer to as déjà vu memorization. Concretely, we show that given the trained model and a crop of a training image containing only the background (e.g., water, sky, grass), it is possible to infer the foreground object with high accuracy or even visually reconstruct it. Furthermore, we show that déjà vu memorization is common to different SSL algorithms, is exacerbated by certain design choices, and cannot be detected by conventional techniques for evaluating representation quality. Our study of déjà vu memorization reveals previously unknown privacy risks in SSL models, as well as suggests potential practical mitigation strategies.
2023-09-20
NeurIPS.cc/2023/Conference (poster)
doi.org
openreview.net
On the Identifiability of Quantized Factors
Vitória Barin-Pacela
Kartik Ahuja
Simon Lacoste-Julien
Pascal Vincent
Disentanglement aims to recover meaningful latent ground-truth factors from the observed distribution solely, and is formalized through the … (see more)theory of identifiability. The identifiability of independent latent factors is proven to be impossible in the unsupervised i.i.d. setting under a general nonlinear map from factors to observations. In this work, however, we demonstrate that it is possible to recover quantized latent factors under a generic nonlinear diffeomorphism. We only assume that the latent factors have independent discontinuities in their density, without requiring the factors to be statistically independent. We introduce this novel form of identifiability, termed quantized factor identifiability, and provide a comprehensive proof of the recovery of the quantized factors.
2023-06-18
ICML.cc/2023/Workshop/SPIGM (poster)
doi.org
proceedings.mlr.press
Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture
Mahmoud Assran
Quentin Duval
Ishan Misra
Piotr Bojanowski
Pascal Vincent
Michael Rabbat
Yann Lecun
Nicolas Ballas
This paper demonstrates an approach for learning highly semantic image representations without relying on hand-crafted data-augmentations. W… (see more)e introduce the Image-based Joint-Embedding Predictive Architecture (I-JEPA), a non-generative approach for self-supervised learning from images. The idea behind I-JEPA is simple: from a single context block, predict the representations of various target blocks in the same image. A core design choice to guide I-JEPA towards producing semantic representations is the masking strategy; specifically, it is crucial to (a) sample target blocks with sufficiently large scale (semantic), and to (b) use a sufficiently informative (spatially distributed) context block. Empirically, when combined with Vision Transformers, we find I-JEPA to be highly scalable. For instance, we train a ViT-Huge/14 on ImageNet using 16 A100 GPUs in under 72 hours to achieve strong downstream performance across a wide range of tasks, from linear classification to object counting and depth prediction.
2023-06-16
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (published)
doi.org
arxiv.org
The Emergence of Argument Structure in Artificial Languages
Tom Bosc
Pascal Vincent
Computational approaches to the study of language emergence can help us understand how natural languages are shaped by cognitive and sociocu… (see more)ltural factors. Previous work focused on tasks where agents refer to a single entity. In contrast, we study how agents predicate, that is, how they express that some relation holds between several entities. We introduce a setup where agents talk about a variable number of entities that can be partially observed by the listener. In the presence of a least-effort pressure, they tend to discuss only entities that are not observed by the listener. Thus we can obtain artificial phrases that denote a single entity, as well as artificial sentences that denote several entities. In natural languages, if we ignore the verb, phrases are usually concatenated, either in a specific order or by adding case markers to form sentences. Our setup allows us to quantify how much this holds in emergent languages using a metric we call concatenability. We also measure transitivity, which quantifies the importance of word order. We demonstrate the usefulness of this new setup and metrics for studying factors that influence argument structure. We compare agents having access to input representations structured into pre-segmented objects with properties, versus unstructured representations. Our results indicate that the awareness of object structure yields a more natural sentence organization.
2022-11-30
Transactions of the Association for Computational Linguistics (published)
doi.org
Accounting for Variance in Machine Learning Benchmarks
Xavier Bouthillier
Pierre Delaunay
Mirko Bronzi
Assya Trofimov
Brennan Nichyporuk
Justin Szeto
Naz Sepah
Edward Raff
Kanika Madan
Vikram Voleti
Samira Ebrahimi Kahou
Vincent Michalski
Dmitriy Serdyuk
Tal Arbel
Chris Pal
Gael Varoquaux
Pascal Vincent
Strong empirical evidence that one machine-learning algorithm A outperforms another one B ideally calls for multiple trials optimizing the l… (see more)earning pipeline over sources of variation such as data sampling, data augmentation, parameter initialization, and hyperparameters choices. This is prohibitively expensive, and corners are cut to reach conclusions. We model the whole benchmarking process, revealing that variance due to data sampling, parameter initialization and hyperparameter choice impact markedly the results. We analyze the predominant comparison methods used today in the light of this variance. We show a counter-intuitive result that adding more sources of variation to an imperfect estimator approaches better the ideal estimator at a 51 times reduction in compute cost. Building on these results, we study the error rate of detecting improvements, on five different deep-learning tasks/architectures. This study leads us to propose recommendations for performance comparisons.
2020-12-31
MLSys (published)
doi.org
arxiv.org
Stochastic Hamiltonian Gradient Methods for Smooth Games
Nicolas Loizou
Hugo Berard
Alexia Jolicoeur-Martineau
Pascal Vincent
Simon Lacoste-Julien
Ioannis Mitliagkas
The success of adversarial formulations in machine learning has brought renewed motivation for smooth games. In this work, we focus on the c… (see more)lass of stochastic Hamiltonian methods and provide the first convergence guarantees for certain classes of stochastic smooth games. We propose a novel unbiased estimator for the stochastic Hamiltonian gradient descent (SHGD) and highlight its benefits. Using tools from the optimization literature we show that SHGD converges linearly to the neighbourhood of a stationary point. To guarantee convergence to the exact solution, we analyze SHGD with a decreasing step-size and we also present the first stochastic variance reduced Hamiltonian method. Our results provide the first global non-asymptotic last-iterate convergence guarantees for the class of stochastic unconstrained bilinear games and for the more general class of stochastic games that satisfy a "sufficiently bilinear" condition, notably including some non-convex non-concave problems. We supplement our analysis with experiments on stochastic bilinear and sufficiently bilinear games, where our theory is shown to be tight, and on simple adversarial machine learning formulations.
2020-07-13
ICML (Accept)
doi.org
proceedings.mlr.press
SVRG for Policy Evaluation with Fewer Gradient Evaluations
Zilun Peng
Ahmed Touati
Pascal Vincent
Doina Precup
Stochastic variance-reduced gradient (SVRG) is an optimization method originally designed for tackling machine learning problems with a fini… (see more)te sum structure. SVRG was later shown to work for policy evaluation, a problem in reinforcement learning in which one aims to estimate the value function of a given policy. SVRG makes use of gradient estimates at two scales. At the slower scale, SVRG computes a full gradient over the whole dataset, which could lead to prohibitive computation costs. In this work, we show that two variants of SVRG for policy evaluation could significantly diminish the number of gradient calculations while preserving a linear convergence speed. More importantly, our theoretical result implies that one does not need to use the entire dataset in every epoch of SVRG when it is applied to policy evaluation with linear function approximation. Our experiments demonstrate large computational savings provided by the proposed methods.
2020-07-10
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (published)
doi.org
arxiv.org