Simon Lacoste-Julien

aristidebaratin@hotmail.com

Biographie

Simon Lacoste-Julien est professeur agrégé au Département d'informatique et de recherche opérationnelle (DIRO) de l'Université de Montréal, membre cofondateur de Mila – Institut québécois d’intelligence artificielle et titulaire d'une chaire en IA Canada-CIFAR. Il dirige également à temps partiel le SAIT AI Lab Montréal.

Ses recherches portent sur l'apprentissage automatique et les mathématiques appliquées, et intègrent des applications à la vision artificielle et au traitement du langage naturel. Il a obtenu une licence en mathématiques, physique et informatique à l’Université McGill, un doctorat en informatique à l’Université de Californie à Berkeley et un postdoctorat à l'Université de Cambridge.

Il a passé quelques années à l'Institut national de recherche en sciences et technologies du numérique (INRIA) et à l'École normale supérieure de Paris en tant que professeur de recherche avant de revenir à Montréal, en 2016, pour répondre à l'appel de Yoshua Bengio et contribuer à la croissance de l'écosystème de l'IA à Montréal.

Étudiants actuels

Reza Babanezhad Harikandeh

Visiteur de recherche indépendant - Samsung SAIT

babanezr@mila.quebec

Aristide Baratin

Visiteur de recherche indépendant - Samsung SAIT

Doctorat - UdeM

vitoria.barin-pacela@mila.quebec

Doctorat - UdeM

simon.dufort-labbe@mila.quebec

Marwa El Halabi

Visiteur de recherche indépendant - Samsung SAIT

marwa.el-halabi@mila.quebec

Doctorat - UdeM

antonio-miguel.gois@mila.quebec

Yash Goyal

Visiteur de recherche indépendant - Samsung SAIT

yashgoyal.yg1@gmail.com

Meraj Hashemizadeh

Collaborateur·rice de recherche - UdeM

merajhse@mila.quebec

fahimeh.hosseini@mila.quebec

Fahimeh HosseiniNoohdani

Collaborateur·rice de recherche - UdeM

pedram.khorsandi@mila.quebec

Pedram Khorsandi

Doctorat - UdeM

Boris Knyazev

Visiteur de recherche indépendant - UdeM

Visiteur de recherche indépendant - Samsung - SAIT

lachaseb@mila.quebec

yukti.makhija@mila.quebec

Lucas Maes

Doctorat - UdeM

lucas.maes@mila.quebec

Doctorat - UdeM

Co-superviseur⋅e :

Ioannis Mitliagkas

Doctorat - UdeM

Co-superviseur⋅e :

Aaron Courville

alan.milligan@mila.quebec

Collaborateur·rice alumni - UdeM

rozhin.nobahari@mila.quebec

Juan Ramirez

Doctorat - UdeM

juan.ramirez@mila.quebec

Doctorat - UdeM

mansi.rankawat@mila.quebec

Visiteur de recherche indépendant - Univeristy of Tübingen

Theo Saulus

Doctorat - UdeM

Co-superviseur⋅e :

Dhanya Sridhar

theo.saulus@mila.quebec

motahareh.sohrabi@mila.quebec

Damien Scieur

Visiteur de recherche indépendant - Samsung SAIT

Collaborateur·rice de recherche - UdeM

tianyue.zhang@mila.quebec

Tianyue (Helen) Zhang

Doctorat - UdeM

Visiteur de recherche indépendant - Samsung SAIT

yan.zhang@mila.quebec

Décodeurs additifs pour l’identification des variables latentes et l’extrapolation du produit cartésien

Billets de blogue

Additive Decoders for Latent Variables Identification and Cartesian-Product Extrapolation

18 mars 2024

par

Sébastien Lachapelle

Divyat Mahajan

Ioannis Mitliagkas

Simon Lacoste-Julien

Lire l'article

Publications

Can We Scale Transformers to Predict Parameters of Diverse ImageNet Models?

Boris Knyazev

Doha Hwang

Pretraining a neural network on a large dataset is becoming a cornerstone in machine learning that is within the reach of only a few communi… (voir plus)ties with large-resources. We aim at an ambitious goal of democratizing pretraining. Towards that goal, we train and release a single neural network that can predict high quality ImageNet parameters of other neural networks. By using predicted parameters for initialization we are able to boost training of diverse ImageNet models available in PyTorch. When transferred to other datasets, models initialized with predicted parameters also converge faster and reach competitive final performance.

2023-07-02

Proceedings of the 40th International Conference on Machine Learning (publié)

CrossSplit: Mitigating Label Noise Memorization through Data Splitting

Jihye Kim

Aristide Baratin

We approach the problem of improving robustness of deep learning algorithms in the presence of label noise. Building upon existing label cor… (voir plus)rection and co-teaching methods, we propose a novel training procedure to mitigate the memorization of noisy labels, called CrossSplit, which uses a pair of neural networks trained on two disjoint parts of the labelled dataset. CrossSplit combines two main ingredients: (i) Cross-split label correction. The idea is that, since the model trained on one part of the data cannot memorize example-label pairs from the other part, the training labels presented to each network can be smoothly adjusted by using the predictions of its peer network; (ii) Cross-split semi-supervised training. A network trained on one part of the data also uses the unlabeled inputs of the other part. Extensive experiments on CIFAR-10, CIFAR-100, Tiny-ImageNet and mini-WebVision datasets demonstrate that our method can outperform the current state-of-the-art in a wide range of noise ratios.

2023-07-02

Proceedings of the 40th International Conference on Machine Learning (publié)

Unlocking Slot Attention by Changing Optimal Transport Costs

David W. Zhang

Gertjan J. Burghouts

Cees G. M. Snoek

Slot attention is a powerful method for object-centric modeling in images and videos. However, its set-equivariance limits its ability to ha… (voir plus)ndle videos with a dynamic number of objects because it cannot break ties. To overcome this limitation, we first establish a connection between slot attention and optimal transport. Based on this new perspective we propose MESH (Minimize Entropy of Sinkhorn): a cross-attention module that combines the tiebreaking properties of unregularized optimal transport with the speed of regularized optimal transport. We evaluate slot attention using MESH on multiple object-centric learning benchmarks and find significant improvements over slot attention in every setting.

2023-07-02

Proceedings of the 40th International Conference on Machine Learning (publié)

On the Identifiability of Quantized Factors

Vitória Barin-Pacela

Kartik Ahuja

Pascal Vincent

Disentanglement aims to recover meaningful latent ground-truth factors from the observed distribution solely, and is formalized through the … (voir plus)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)

A Survey of Self-Supervised and Few-Shot Object Detection

Gabriel Huang

Issam Hadj Laradji

David Vázquez

Pau Rodríguez

Labeling data is often expensive and time-consuming, especially for tasks such as object detection and instance segmentation, which require … (voir plus)dense labeling of the image. While few-shot object detection is about training a model on novel (unseen) object classes with little data, it still requires prior training on many labeled examples of base (seen) classes. On the other hand, self-supervised methods aim at learning representations from unlabeled data which transfer well to downstream tasks such as object detection. Combining few-shot and self-supervised object detection is a promising research direction. In this survey, we review and characterize the most recent approaches on few-shot and self-supervised object detection. Then, we give our main takeaways and discuss future research directions. Project page: https://gabrielhuang.github.io/fsod-survey/.

2023-03-31

IEEE Transactions on Pattern Analysis and Machine Intelligence (publié)

Reza Babanezhad Harikandeh

arxiv.org

SVRG meets AdaGrad: painless variance reduction

Benjamin Dubois-Taine

Sharan Vaswani

Mark Schmidt

2022-11-09

Machine Learning (publié)

arxiv.org

Controlled Sparsity via Constrained Optimization or: How I Learned to Stop Tuning Penalties and Love Constraints

Jose Gallego-Posada

The performance of trained neural networks is robust to harsh levels of pruning. Coupled with the ever-growing size of deep learning models,… (voir plus) this observation has motivated extensive research on learning sparse models. In this work, we focus on the task of controlling the level of sparsity when performing sparse learning. Existing methods based on sparsity-inducing penalties involve expensive trial-and-error tuning of the penalty factor, thus lacking direct control of the resulting model sparsity. In response, we adopt a constrained formulation: using the gate mechanism proposed by Louizos et al. (2018), we formulate a constrained optimization problem where sparsification is guided by the training objective and the desired sparsity target in an end-to-end fashion. Experiments on CIFAR-{10, 100}, TinyImageNet, and ImageNet using WideResNet and ResNet{18, 50} models validate the effectiveness of our proposal and demonstrate that we can reliably achieve pre-determined sparsity targets without compromising on predictive performance.

2022-10-30

NeurIPS.cc/2022/Conference (accepté)

Partial Disentanglement via Mechanism Sparsity

Sébastien Lachapelle

2022-07-08

auai.org/UAI/2022/Workshop/CRL (présentation orale)

Bayesian Structure Learning with Generative Flow Networks

In Bayesian structure learning, we are interested in inferring a distribution over the directed acyclic graph (DAG) structure of Bayesian ne… (voir plus)tworks, from data. Defining such a distribution is very challenging, due to the combinatorially large sample space, and approximations based on MCMC are often required. Recently, a novel class of probabilistic models, called Generative Flow Networks (GFlowNets), have been introduced as a general framework for generative modeling of discrete and composite objects, such as graphs. In this work, we propose to use a GFlowNet as an alternative to MCMC for approximating the posterior distribution over the structure of Bayesian networks, given a dataset of observations. Generating a sample DAG from this approximate distribution is viewed as a sequential decision problem, where the graph is constructed one edge at a time, based on learned transition probabilities. Through evaluation on both simulated and real data, we show that our approach, called DAG-GFlowNet, provides an accurate approximation of the posterior over DAGs, and it compares favorably against other methods based on MCMC or variational inference.

2022-05-19

Conference on Uncertainty in Artificial Intelligence (poster)

Multiset-Equivariant Set Prediction with Approximate Implicit Differentiation

David W. Zhang

Gertjan J. Burghouts

Cees G. M. Snoek

Most set prediction models in deep learning use set-equivariant operations, but they actually operate on multisets. We show that set-equivar… (voir plus)iant functions cannot represent certain functions on multisets, so we introduce the more appropriate notion of multiset-equivariance. We identify that the existing Deep Set Prediction Network (DSPN) can be multiset-equivariant without being hindered by set-equivariance and improve it with approximate implicit differentiation, allowing for better optimization while being faster and saving memory. In a range of toy experiments, we show that the perspective of multiset-equivariance is beneficial and that our changes to DSPN achieve better results in most cases. On CLEVR object property prediction, we substantially improve over the state-of-the-art Slot Attention from 8% to 77% in one of the strictest evaluation metrics because of the benefits made possible by implicit differentiation.

2022-04-24

International Conference on Learning Representations (Accept (Poster))

Online Adversarial Attacks

Avishek Joey Bose

William L. Hamilton

Adversarial attacks expose important vulnerabilities of deep learning models, yet little attention has been paid to settings where data arri… (voir plus)ves as a stream. In this paper, we formalize the online adversarial attack problem, emphasizing two key elements found in real-world use-cases: attackers must operate under partial knowledge of the target model, and the decisions made by the attacker are irrevocable since they operate on a transient data stream. We first rigorously analyze a deterministic variant of the online threat model by drawing parallels to the well-studied

2022-04-24

International Conference on Learning Representations (Accept (Poster))

On the Convergence of Continuous Constrained Optimization for Structure Learning

Ignavier Ng

Sébastien Lachapelle

Nan Rosemary Ke

Kun Zhang

Recently, structure learning of directed acyclic graphs (DAGs) has been formulated as a continuous optimization problem by leveraging an alg… (voir plus)ebraic characterization of acyclicity. The constrained problem is solved using the augmented Lagrangian method (ALM) which is often preferred to the quadratic penalty method (QPM) by virtue of its standard convergence result that does not require the penalty coefficient to go to infinity, hence avoiding ill-conditioning. However, the convergence properties of these methods for structure learning, including whether they are guaranteed to return a DAG solution, remain unclear, which might limit their practical applications. In this work, we examine the convergence of ALM and QPM for structure learning in the linear, nonlinear, and confounded cases. We show that the standard convergence result of ALM does not hold in these settings, and demonstrate empirically that its behavior is akin to that of the QPM which is prone to ill-conditioning. We further establish the convergence guarantee of QPM to a DAG solution, under mild conditions. Lastly, we connect our theoretical results with existing approaches to help resolve the convergence issue, and verify our findings in light of an empirical comparison of them.

2022-03-27

International Conference on Artificial Intelligence and Statistics (Accept (Poster))