Portrait de Simon Lacoste-Julien

Simon Lacoste-Julien

Membre académique principal
slacoste@mila.quebec
Chaire en IA Canada-CIFAR
Directeur scientifique adjoint, Mila, Professeur agrégé, Université de Montréal, Département d'informatique et de recherche opérationnelle
Vice-président et directeur de laboratoire, Samsung Advanced Institute of Technology (SAIT) AI Lab, Montréal
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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

Alexia Jolicoeur-Martineau
Visiteur de recherche indépendant - Samsung SAIT
jolicoea@mila.quebec
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Antonio Gois
Doctorat - Université de Montréal
antonio-miguel.gois@mila.quebec
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Aristide Baratin
Visiteur de recherche indépendant - Samsung SAIT
baratina@mila.quebec
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Github
Google Scholar
Boris Knyazev
Visiteur de recherche indépendant - Université de Montréal
knyazevb@mila.quebec
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Damien Scieur
Visiteur de recherche indépendant - Samsung SAIT
scieurda@mila.quebec
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George Orfanides
Doctorat - McGill University
Superviseur⋅e principal⋅e :
Adam M. Oberman
george.orfanides@mila.quebec
Jaewoo Lee
Visiteur de recherche indépendant - Pohang University of Science and Technology in Pohang, Korea
jaewoo.lee@mila.quebec
Jose Gallego Posada
Doctorat - Université de Montréal
gallegoj@mila.quebec
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Juan Ramirez
Doctorat - Université de Montréal
juan.ramirez@mila.quebec
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Kiho Cho
Visiteur de recherche indépendant - Samsung SAIT
kiho.cho@mila.quebec
Kwon Kisoo
Visiteur de recherche indépendant - Seoul National University, Korea
kwon.kisoo@mila.quebec
Lucas Maes
Doctorat - Université de Montréal
lucas.maes@mila.quebec
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Mansi Rankawat
Doctorat - Université de Montréal
mansi.rankawat@mila.quebec
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Marwa El Halabi
Visiteur de recherche indépendant - Samsung SAIT
marwa.el-halabi@mila.quebec
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Meraj Hashemizadeh
Collaborateur·rice de recherche - Université de Montréal
merajhse@mila.quebec
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Michelle Liu
Collaborateur·rice de recherche
liumiche@mila.quebec
Motahareh Sohrabi
Maîtrise recherche - Université de Montréal
motahareh.sohrabi@mila.quebec
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Pedram Khorsandi
Doctorat - Université de Montréal
pedram.khorsandi@mila.quebec
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Quentin Bertrand
Postdoctorat - Université de Montréal
Superviseur⋅e principal⋅e :
Gauthier Gidel
quentin.bertrand@mila.quebec
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Reza Babanezhad Harikandeh
Visiteur de recherche indépendant - Samsung SAIT
babanezr@mila.quebec
Rozhin Nobahari
Maîtrise recherche - Université de Montréal
rozhin.nobahari@mila.quebec
Sébastien Lachapelle
Doctorat - Université de Montréal
lachaseb@mila.quebec
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Helen Zhang
Doctorat - Université de Montréal
tianyue.zhang@mila.quebec
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Vitoria Barin Pacela
Doctorat - Université de Montréal
vitoria.barin-pacela@mila.quebec
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Yan Zhang
Visiteur de recherche indépendant - Samsung SAIT
yan.zhang@mila.quebec
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Google Scholar
Yash Goyal
Visiteur de recherche indépendant - Samsung SAIT
yash.goyal@mila.quebec
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Publications

Promoting Exploration in Memory-Augmented Adam using Critical Momenta
Pranshu Malviya
Goncalo Mordido
Aristide Baratin
Reza Babanezhad Harikandeh
Jerry Huang
Simon Lacoste-Julien
Razvan Pascanu
Sarath Chandar Anbil Parthipan
Adaptive gradient-based optimizers, particularly Adam, have left their mark in training large-scale deep learning models. The strength of su… (voir plus)ch optimizers is that they exhibit fast convergence while being more robust to hyperparameter choice. However, they often generalize worse than non-adaptive methods. Recent studies have tied this performance gap to flat minima selection: adaptive methods tend to find solutions in sharper basins of the loss landscape, which in turn hurts generalization. To overcome this issue, we propose a new memory-augmented version of Adam that promotes exploration towards flatter minima by using a buffer of critical momentum terms during training. Intuitively, the use of the buffer makes the optimizer overshoot outside the basin of attraction if it is not wide enough. We empirically show that our method improves the performance of several variants of Adam on standard supervised language modelling and image classification tasks.
2024-06-09
TMLR (accepté)
doi.org
openreview.net
Weight-Sharing Regularization
Mehran Shakerinava
Motahareh Sohrabi
Siamak Ravanbakhsh
Simon Lacoste-Julien
2024-04-18
Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (publié)
doi.org
arxiv.org
PopulAtion Parameter Averaging (PAPA)
Alexia Jolicoeur-Martineau
Emy Gervais
Kilian FATRAS
Yan Zhang
Simon Lacoste-Julien
2024-04-05
TMLR (accepté)
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… (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.
2024-03-15
Proceedings of the Third Conference on Causal Learning and Reasoning (publié)
proceedings.mlr.press
arxiv.org
Balancing Act: Constraining Disparate Impact in Sparse Models
Meraj Hashemizadeh
Juan Ramirez
Rohan Sukumaran
Golnoosh Farnadi
Simon Lacoste-Julien
Jose Gallego-Posada
Model pruning is a popular approach to enable the deployment of large deep learning models on edge devices with restricted computational or … (voir plus)storage capacities. Although sparse models achieve performance comparable to that of their dense counterparts at the level of the entire dataset, they exhibit high accuracy drops for some data sub-groups. Existing methods to mitigate this disparate impact induced by pruning (i) rely on surrogate metrics that address the problem indirectly and have limited interpretability; or (ii) scale poorly with the number of protected sub-groups in terms of computational cost. We propose a constrained optimization approach that directly addresses the disparate impact of pruning: our formulation bounds the accuracy change between the dense and sparse models, for each sub-group. This choice of constraints provides an interpretable success criterion to determine if a pruned model achieves acceptable disparity levels. Experimental results demonstrate that our technique scales reliably to problems involving large models and hundreds of protected sub-groups.
2024-01-16
ICLR.cc/2024/Conference (poster)
doi.org
openreview.net
Nonparametric Partial Disentanglement via Mechanism Sparsity: Sparse Actions, Interventions and Sparse Temporal Dependencies
Sébastien Lachapelle
Pau Rodriguez
Yash Sharma
Katie Everett
Rémi LE PRIOL
Alexandre Lacoste
Simon Lacoste-Julien
2024-01-10
ArXiv (prépublication)
doi.org
arxiv.org
Additive Decoders for Latent Variables Identification and Cartesian-Product Extrapolation
Sébastien Lachapelle
Divyat Mahajan
Ioannis Mitliagkas
Simon Lacoste-Julien
We tackle the problems of latent variables identification and "out-of-support'' image generation in representation learning. We show that bo… (voir plus)th are possible for a class of decoders that we call additive, which are reminiscent of decoders used for object-centric representation learning (OCRL) and well suited for images that can be decomposed as a sum of object-specific images. We provide conditions under which exactly solving the reconstruction problem using an additive decoder is guaranteed to identify the blocks of latent variables up to permutation and block-wise invertible transformations. This guarantee relies only on very weak assumptions about the distribution of the latent factors, which might present statistical dependencies and have an almost arbitrarily shaped support. Our result provides a new setting where nonlinear independent component analysis (ICA) is possible and adds to our theoretical understanding of OCRL methods. We also show theoretically that additive decoders can generate novel images by recombining observed factors of variations in novel ways, an ability we refer to as Cartesian-product extrapolation. We show empirically that additivity is crucial for both identifiability and extrapolation on simulated data.
openreview.net
Can We Scale Transformers to Predict Parameters of Diverse ImageNet Models?
Boris Knyazev
DOHA HWANG
Simon Lacoste-Julien
2023-07-03
Proceedings of the 40th International Conference on Machine Learning (publié)
doi.org
openreview.net
CrossSplit: Mitigating Label Noise Memorization through Data Splitting
Jihye Kim
Aristide Baratin
Yan Zhang
Simon Lacoste-Julien
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 labeled 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. The project page is at https://rlawlgul.github.io/.
2023-07-03
Proceedings of the 40th International Conference on Machine Learning (publié)
doi.org
openreview.net
Unlocking Slot Attention by Changing Optimal Transport Costs
Yan Zhang
David W Zhang
Simon Lacoste-Julien
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.
2023-07-03
Proceedings of the 40th International Conference on Machine Learning (publié)
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 … (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.
2023-06-28
ArXiv (prépublication)
arxiv.org
arxiv.org
Identifiability of Discretized Latent Coordinate Systems via Density Landmarks Detection
Vitória Barin-Pacela
Kartik Ahuja
Simon Lacoste-Julien
Pascal Vincent
Disentanglement aims to recover meaningful latent ground-truth factors from only the observed distribution. Identifiability provides the the… (voir plus)oretical grounding for disentanglement to be well-founded. Unfortunately, unsupervised identifiability of independent latent factors is a theoretically proven impossibility in the i.i.d. setting under a general nonlinear smooth map from factors to observations. In this work, we show that, remarkably, it is possible to recover discretized latent coordinates under a highly generic nonlinear smooth mapping (a diffeomorphism) without any additional inductive bias on the mapping. This is, assuming that latent density has axis-aligned discontinuity landmarks, but without making the unrealistic assumption of statistical independence of the factors. We introduce this novel form of identifiability, termed quantized coordinate identifiability , and provide a comprehensive proof of the recovery of discretized coordinates.
2023-06-19
ICML.cc/2023/Workshop/SPIGM (poster)
doi.org
openreview.net