Portrait of Eugene Belilovsky is unavailable

Eugene Belilovsky

Associate Academic Member
Assistant Professor, Concordia University, Department of Computer Science and Software Engineering
Adjunct Professor, Université de Montréal, Department of Computer Science and Operations Research
Research Topics
Deep Learning
Distributed Systems
Optimization

Biography

Eugene Belilovsky is an assistant professor in the Department of Computer Science and Software Engineering at Concordia University.

He is also an associate academic member of Mila – Quebec Artificial Intelligence Institute and an adjunct professor at Université de Montréal.

Belilovsky’s research specialties lie in computer vision and deep learning. His current interests include continual learning and few-shot learning, along with applications of these aspects at the intersection of computer vision and language processing.

Current Students

Collaborating Alumni
Co-supervisor :
Master's Research - Concordia University
PhD - Concordia University
Research Intern - Concordia University
Master's Research - Concordia University
Master's Research - Concordia University
Master's Research - Université de Montréal
Co-supervisor :
Master's Research - Concordia University
PhD - Concordia University
Co-supervisor :
Research Intern - Concordia University University
PhD - Concordia University
PhD - Concordia University
Postdoctorate - Concordia University
Co-supervisor :
PhD - Concordia University
Co-supervisor :
Master's Research - Concordia University
Co-supervisor :
Collaborating researcher - Concordia University
Co-supervisor :
PhD - Concordia University
Co-supervisor :
PhD - Université de Montréal
Principal supervisor :
Collaborating researcher - Université de Montréal
Principal supervisor :
Master's Research - Concordia University
PhD - Concordia University
Master's Research - Concordia University

Publications

Harmony in Diversity: Merging Neural Networks with Canonical Correlation Analysis
Stefan Horoi
Albert Manuel Orozco Camacho
Simple and Scalable Strategies to Continually Pre-train Large Language Models
Adam Ibrahim
Benjamin Thérien
Kshitij Gupta
Mats Leon Richter
Quentin Gregory Anthony
Timothee LESORT
Simulating federated learning for steatosis detection using ultrasound images
Yue Qi
Pedro Vianna
Alexandre Cadrin-Chênevert
Katleen Blanchet
Emmanuel Montagnon
Louis-Antoine Mullie
Guy Cloutier
Michael Chassé
An Tang
$\mu$LO: Compute-Efficient Meta-Generalization of Learned Optimizers
Benjamin Thérien
Charles-Étienne Joseph
Boris Knyazev
Edouard Oyallon
WASH: Train your Ensemble with Communication-Efficient Weight Shuffling, then Average
Louis Fournier
Adel Nabli
Masih Aminbeidokhti
Marco Pedersoli
Edouard Oyallon
The performance of deep neural networks is enhanced by ensemble methods, which average the output of several models. However, this comes at … (see more)an increased cost at inference. Weight averaging methods aim at balancing the generalization of ensembling and the inference speed of a single model by averaging the parameters of an ensemble of models. Yet, naive averaging results in poor performance as models converge to different loss basins, and aligning the models to improve the performance of the average is challenging. Alternatively, inspired by distributed training, methods like DART and PAPA have been proposed to train several models in parallel such that they will end up in the same basin, resulting in good averaging accuracy. However, these methods either compromise ensembling accuracy or demand significant communication between models during training. In this paper, we introduce WASH, a novel distributed method for training model ensembles for weight averaging that achieves state-of-the-art image classification accuracy. WASH maintains models within the same basin by randomly shuffling a small percentage of weights during training, resulting in diverse models and lower communication costs compared to standard parameter averaging methods.
AdaFisher: Adaptive Second Order Optimization via Fisher Information
Damien Martins Gomes
Yanlei Zhang
Mahdi S. Hosseini
First-order optimization methods are currently the mainstream in training deep neural networks (DNNs). Optimizers like Adam incorporate limi… (see more)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 are still limited due to increased per-iteration computations and suboptimal accuracy compared to the first order methods. We present AdaFisher--an adaptive second-order optimizer that leverages a block-diagonal approximation to the Fisher information matrix for adaptive gradient preconditioning. AdaFisher aims to bridge the gap between enhanced convergence 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 modelling and stand out for its stability and robustness in hyperparameter tuning. We demonstrate that AdaFisher outperforms the SOTA optimizers in terms of both accuracy and convergence speed. Code available from \href{https://github.com/AtlasAnalyticsLab/AdaFisher}{https://github.com/AtlasAnalyticsLab/AdaFisher}
Harmony in Diversity: Merging Neural Networks with Canonical Correlation Analysis
Stefan Horoi
Albert Manuel Orozco Camacho
Ensembling multiple models enhances predictive performance by utilizing the varied learned features of the different models but incurs signi… (see more)ficant computational and storage costs. Model fusion, which combines parameters from multiple models into one, aims to mitigate these costs but faces practical challenges due to the complex, non-convex nature of neural network loss landscapes, where learned minima are often separated by high loss barriers. Recent works have explored using permutations to align network features, reducing the loss barrier in parameter space. However, permutations are restrictive since they assume a one-to-one mapping between the different models' neurons exists. We propose a new model merging algorithm, CCA Merge, which is based on Canonical Correlation Analysis and aims to maximize the correlations between linear combinations of the model features. We show that our method of aligning models leads to better performances than past methods when averaging models trained on the same, or differing data splits. We also extend this analysis into the harder many models setting where more than 2 models are merged, and we find that CCA Merge works significantly better in this setting than past methods.
Adversarial Attacks on the Interpretation of Neuron Activation Maximization
G'eraldin Nanfack
Alexander Fulleringer
Jonathan Marty
Michael Eickenberg
Feature visualization is one of the most popular techniques used to interpret the internal behavior of individual units of trained deep neur… (see more)al networks. Based on activation maximization, they consist of finding synthetic or natural inputs that maximize neuron activations. This paper introduces an optimization framework that aims to deceive feature visualization through adversarial model manipulation. It consists of finetuning a pre-trained model with a specifically introduced loss that aims to maintain model performance, while also significantly changing feature visualization. We provide evidence of the success of this manipulation on several pre-trained models for the classification task with ImageNet.
Simple and Scalable Strategies to Continually Pre-train Large Language Models
Adam Ibrahim
Benjamin Thérien
Kshitij Gupta
Mats Leon Richter
Quentin Anthony
Timothee LESORT
Large language models (LLMs) are routinely pre-trained on billions of tokens, only to start the process over again once new data becomes ava… (see more)ilable. A much more efficient solution is to continually pre-train these models, saving significant compute compared to re-training. However, the distribution shift induced by new data typically results in degraded performance on previous data or poor adaptation to the new data. In this work, we show that a simple and scalable combination of learning rate (LR) re-warming, LR re-decaying, and replay of previous data is sufficient to match the performance of fully re-training from scratch on all available data, as measured by the final loss and the average score on several language model (LM) evaluation benchmarks. Specifically, we show this for a weak but realistic distribution shift between two commonly used LLM pre-training datasets (English
Harmony in Diversity: Merging Neural Networks with Canonical Correlation Analysis
Stefan Horoi
Albert Manuel Orozco Camacho
Ensembling multiple models enhances predictive performance by utilizing the varied learned features of the different models but incurs signi… (see more)ficant computational and storage costs. Model fusion, which combines parameters from multiple models into one, aims to mitigate these costs but faces practical challenges due to the complex, non-convex nature of neural network loss landscapes, where learned minima are often separated by high loss barriers. Recent works have explored using permutations to align network features, reducing the loss barrier in parameter space. However, permutations are restrictive since they assume a one-to-one mapping between the different models' neurons exists. We propose a new model merging algorithm, CCA Merge, which is based on Canonical Correlation Analysis and aims to maximize the correlations between linear combinations of the model features. We show that our method of aligning models leads to better performances than past methods when averaging models trained on the same, or differing data splits. We also extend this analysis into the harder many models setting where more than 2 models are merged, and we find that CCA Merge works significantly better in this setting than past methods.
Channel-Selective Normalization for Label-Shift Robust Test-Time Adaptation
Pedro Vianna
Muawiz Chaudhary
Paria Mehrbod
An Tang
Guy Cloutier
Michael Eickenberg
Deep neural networks have useful applications in many different tasks, however their performance can be severely affected by changes in the … (see more)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.
Model Breadcrumbs: Scaling Multi-Task Model Merging with Sparse Masks
MohammadReza Davari