Eugene Belilovsky

Membre académique associé

Professeur adjoint, Concordia University, Département d'informatique et de génie logiciel

Professeur associé, Université de Montréal, Département d'informatique et de recherche opérationnelle

Sujets de recherche

Apprentissage continu

Apprentissage fédéré

Apprentissage profond

Grands modèles de langage (LLM)

Optimisation

Biographie

Eugene Belilovsky est professeur adjoint au Département d'informatique et de génie logiciel de l'Université Concordia. Il est également membre associé de Mila – Institut québécois d’intelligence artificielle et professeur adjoint à l'Université de Montréal. Ses travaux se concentrent sur la vision par ordinateur et l'apprentissage profond. Ses intérêts de recherche actuels comprennent l'apprentissage continu, l'apprentissage à partir de peu de données (few-shot learning) et leurs applications au carrefour de la vision par ordinateur et du traitement du langage.

Étudiants actuels

Pietro Cagnasso

Doctorat - Concordia

Co-superviseur⋅e :

Irina Rish

Site web

Github

Xiao Huang

Maîtrise recherche - Concordia

Co-superviseur⋅e :

Paul Janson

Doctorat - Concordia

Co-superviseur⋅e :

Maîtrise recherche - Concordia

Co-superviseur⋅e :

Irina Rish

Site web

Github

Gwen Legate

Doctorat - Concordia

Co-superviseur⋅e :

Maîtrise recherche - Concordia

Co-superviseur⋅e :

Doctorat - Concordia

Adel Nabli

Doctorat - Concordia

Github

Google Scholar

Geraldin Nanfack

Postdoctorat - Concordia

Co-superviseur⋅e :

Albert Orozco Camacho

Doctorat - Concordia

Co-superviseur⋅e :

Doctorat - Concordia

Co-superviseur⋅e :

Irina Rish

Benjamin Therien

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Postdoctorat - UdeM

Superviseur⋅e principal⋅e :

Doctorat - Concordia

Co-superviseur⋅e :

Publications

Unsupervised Test-Time Adaptation for Hepatic Steatosis Grading Using Ultrasound B-Mode Images

Michael Eickenberg

An Tang

Guy Cloutier

Ultrasound (US) is considered a key modality for the clinical assessment of hepatic steatosis (i.e., fatty liver) due to its noninvasiveness… (voir plus) and availability. Deep learning methods have attracted considerable interest in this field, as they are capable of learning patterns in a collection of images and achieve clinically comparable levels of accuracy in steatosis grading. However, variations in patient populations, acquisition protocols, equipment, and operator expertise across clinical sites can introduce domain shifts that reduce model performance when applied outside the original training setting. In response, unsupervised domain adaptation techniques are being investigated to address these shifts, allowing models to generalize more effectively across diverse clinical environments. In this work, we propose a test-time batch normalization (TTN) technique designed to handle domain shift, especially for changes in label distribution, by adapting selected features of batch normalization (BatchNorm) layers in a trained convolutional neural network model. This approach operates in an unsupervised manner, allowing robust adaptation to new distributions without access to label data. The method was evaluated on two abdominal US datasets collected at different institutions, assessing its capability in mitigating domain shift for hepatic steatosis classification. The proposed method reduced the mean absolute error in steatosis grading by 37% and improved the area under the receiver operating characteristic curves (AUC) for steatosis detection from 0.78 to 0.97, compared to nonadapted models. These findings demonstrate the potential of the proposed method to address domain shift in US-based hepatic steatosis diagnosis, minimizing risks associated with deploying trained models in various clinical settings.

2025-03-25

IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control (publié)

doi.org

Adaptive Local Training in Federated Learning.

Donald Shenaj

Eugene Belilovsky

Pietro Zanuttigh

In federated learning multiple clients collaboratively train a global machine learning model by exchanging their locally trained model weigh… (voir plus)ts instead of raw data. In the standard setting, every client trains its local model for the same number of epochs. We introduce ALT (Adaptive Local Training), a simple yet effective feedback mechanism that can be introduced on top of any federated learning scheme at the client side to limit unnecessary and degrading computations. ALT dynamically adjusts the number of training epochs for each client based on the similarity between the local representation and the global one, ensuring that well-aligned clients can train longer without experiencing client drift while in case of too large drifts the training is stopped earlier. We evaluated ALT on federated partitions of the CIFAR-10 and Tiny-ImageNet datasets, demonstrating its effectiveness in improving both model convergence speed and accuracy. The code is available at https://github.com/LTTM/ALT.

2025-03-04

ICLR.cc/2025/Workshop/MCDC (accepté)

doi.org

openreview.net

Large language models deconstruct the clinical intuition behind diagnosing autism

Emmett Rabot

Laurent Mottron

2025-02-28

Cell (publié)

doi.org

Channel-Selective Normalization for Label-Shift Robust Test-Time Adaptation

Pedro Vianna

Muawiz Chaudhary

Paria Mehrbod

An Tang

Guy Cloutier

Guy Wolf

Michael Eickenberg

Eugene Belilovsky

Deep neural networks have useful applications in many different tasks, however their performance can be severely affected by changes in the … (voir plus)data distribution. For example, in the biomedical field, their performance can be affected by changes in the data (different machines, populations) between training and test datasets. To ensure robustness and generalization to real-world scenarios, test-time adaptation has been recently studied as an approach to adjust models to a new data distribution during inference. Test-time batch normalization is a simple and popular method that achieved compelling performance on domain shift benchmarks. It is implemented by recalculating batch normalization statistics on test batches. Prior work has focused on analysis with test data that has the same label distribution as the training data. However, in many practical applications this technique is vulnerable to label distribution shifts, sometimes producing catastrophic failure. This presents a risk in applying test time adaptation methods in deployment. We propose to tackle this challenge by only selectively adapting channels in a deep network, minimizing drastic adaptation that is sensitive to label shifts. Our selection scheme is based on two principles that we empirically motivate: (1) later layers of networks are more sensitive to label shift (2) individual features can be sensitive to specific classes. We apply the proposed technique to three classification tasks, including CIFAR10-C, Imagenet-C, and diagnosis of fatty liver, where we explore both covariate and label distribution shifts. We find that our method allows to bring the benefits of TTA while significantly reducing the risk of failure common in other methods, while being robust to choice in hyperparameters.

2025-02-16

Conference on Lifelong Learning Agents (publié)

doi.org

proceedings.mlr.press

FairDropout: Using Example-Tied Dropout to Enhance Generalization of Minority Groups

Geraldin Nanfack

Eugene Belilovsky

2025-02-09

ArXiv (prépublication)

doi.org

arxiv.org

Accelerating Training with Neuron Interaction and Nowcasting Networks

Neural network training can be accelerated when a learnable update rule is used in lieu of classic adaptive optimizers (e.g. Adam). However,… (voir plus) learnable update rules can be costly and unstable to train and use. Recently, Jang et al. (2023) proposed a simpler approach to accelerate training based on weight nowcaster networks (WNNs). In their approach, Adam is used for most of the optimization steps and periodically, only every few steps, a WNN nowcasts (predicts near future) parameters. We improve WNNs by proposing neuron interaction and nowcasting (NiNo) networks. In contrast to WNNs, NiNo leverages neuron connectivity and graph neural networks to more accurately nowcast parameters. We further show that in some networks, such as Transformers, modeling neuron connectivity accurately is challenging. We address this and other limitations, which allows NiNo to accelerate Adam training by up to 50% in vision and language tasks.

2025-01-21

ICLR.cc/2025/Conference (poster)

doi.org

openreview.net

AdaFisher: Adaptive Second Order Optimization via Fisher Information

Damien Martins Gomes

Yanlei Zhang

Eugene Belilovsky

Guy Wolf

Mahdi S. Hosseini

First-order optimization methods are currently the mainstream in training deep neural networks (DNNs). Optimizers like Adam incorporate limi… (voir plus)ted curvature information by employing the diagonal matrix preconditioning of the stochastic gradient during the training. Despite their widespread, second-order optimization algorithms exhibit superior convergence properties compared to their first-order counterparts e.g. Adam and SGD. However, their practicality in training DNNs is still limited due to increased per-iteration computations compared to the first-order methods. We present \emph{AdaFisher}--an adaptive second-order optimizer that leverages a \emph{diagonal block-Kronecker} approximation of the Fisher information matrix for adaptive gradient preconditioning. AdaFisher aims to bridge the gap between enhanced \emph{convergence/generalization} capabilities and computational efficiency in second-order optimization framework for training DNNs. Despite the slow pace of second-order optimizers, we showcase that AdaFisher can be reliably adopted for image classification, language modeling and stands out for its stability and robustness in hyper-parameter tuning. We demonstrate that AdaFisher \textbf{outperforms the SOTA optimizers} in terms of both accuracy and convergence speed. Code is available from https://github.com/AtlasAnalyticsLab/AdaFisher.

2025-01-21

ICLR.cc/2025/Conference (poster)

doi.org

openreview.net

Celo: Training Versatile Learned Optimizers on a Compute Diet

Learned optimization has emerged as a promising alternative to hand-crafted optimizers, with the potential to discover stronger learned upda… (voir plus)te rules that enable faster, hyperparameter-free training of neural networks. A critical element for practically useful learned optimizers, that can be used off-the-shelf after meta-training, is strong meta-generalization: the ability to apply the optimizers to new tasks. Recent state-of-the-art work in learned optimizers, VeLO (Metz et al., 2022), requires a large number of highly diverse meta-training tasks along with massive computational resources, 4000 TPU months, to achieve meta-generalization. This makes further improvements to such learned optimizers impractical. In this work, we identify several key elements in learned optimizer architectures and meta-training procedures that can lead to strong meta-generalization. We also propose evaluation metrics to reliably assess quantitative performance of an optimizer at scale on a set of evaluation tasks. Our proposed approach, Celo, makes a significant leap in improving the meta-generalization performance of learned optimizers and also outperforms tuned state-of-the-art optimizers on a diverse set of out-of-distribution tasks, despite being meta-trained for just 24 GPU hours.

2025-01-21

ArXiv (prépublication)

doi.org

openreview.net

Can We Learn Communication-Efficient Optimizers?

Charles-Etienne Joseph

2024-12-31

Trans. Mach. Learn. Res. (publié)

doi.org

openreview.net

PETRA: Parallel End-to-End Training of Reversible Architectures

Stéphane Rivaud

Louis Fournier

Thomas Pumir

Eugene Belilovsky

Mickael Eickenberg

Edouard Oyallon

Reversible architectures have been shown to be capable of performing on par with their non-reversible architectures, being applied in deep l… (voir plus)earning for memory savings and generative modeling. In this work, we show how reversible architectures can solve challenges in parallelizing deep model training. We introduce PETRA, a novel alternative to backpropagation for parallelizing gradient computations. PETRA facilitates effective model parallelism by enabling stages (i.e., a set of layers) to compute independently on different devices, while only needing to communicate activations and gradients between each other. By decoupling the forward and backward passes and keeping a single updated version of the parameters, the need for weight stashing is also removed. We develop a custom autograd-like training framework for PETRA, and we demonstrate its effectiveness on CIFAR-10, ImageNet32, and ImageNet, achieving competitive accuracies comparable to backpropagation using ResNet-18, ResNet-34, and ResNet-50 models.

2024-12-31

ICLR (publié)

doi.org

openreview.net

Non-Uniform Parameter-Wise Model Merging

Albert Manuel Orozco Camacho

Stefan Horoi

Guy Wolf

Eugene Belilovsky

Combining multiple machine learning models has long been a technique for enhancing performance, particularly in distributed settings. Tradit… (voir plus)ional approaches, such as model ensembles, work well, but are expensive in terms of memory and compute. Recently, methods based on averaging model parameters have achieved good results in some settings and have gained popularity. However, merging models initialized differently that do not share a part of their training trajectories can yield worse results than simply using the base models, even after aligning their neurons. In this paper, we introduce a novel approach, Non-uniform Parameter-wise Model Merging, or NP Merge, which merges models by learning the contribution of each parameter to the final model using gradient-based optimization. We empirically demonstrate the effectiveness of our method for merging models of various architectures in multiple settings, outperforming past methods. We also extend NP Merge to handle the merging of multiple models, showcasing its scalability and robustness.

2024-12-14

2024 IEEE International Conference on Big Data (BigData) (publié)

doi.org

arxiv.org

Sketch-guided Cage-based 3D Gaussian Splatting Deformation

Tianhao Xie

Noam Aigerman

Eugene Belilovsky

Tiberiu Popa

3D Gaussian Splatting (GS) is one of the most promising novel 3D representations that has received great interest in computer graphics and c… (voir plus)omputer vision. While various systems have introduced editing capabilities for 3D GS, such as those guided by text prompts, fine-grained control over deformation remains an open challenge. In this work, we present a novel sketch-guided 3D GS deformation system that allows users to intuitively modify the geometry of a 3D GS model by drawing a silhouette sketch from a single viewpoint. Our approach introduces a new deformation method that combines cage-based deformations with a variant of Neural Jacobian Fields, enabling precise, fine-grained control. Additionally, it leverages large-scale 2D diffusion priors and ControlNet to ensure the generated deformations are semantically plausible. Through a series of experiments, we demonstrate the effectiveness of our method and showcase its ability to animate static 3D GS models as one of its key applications.

2024-11-18

ArXiv (prépublication)

doi.org

arxiv.org

Mila Techaide 2026

Propulsion d'entrepreneurs scientifiques

Avantage IA : productivité dans la fonction publique

Eugene Belilovsky

Biographie

Étudiants actuels

Publications

Mila Techaide 2026

Propulsion d'entrepreneurs scientifiques

Avantage IA : productivité dans la fonction publique

Mots-clés populaires:

Eugene Belilovsky

Biographie

Étudiants actuels

Publications