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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

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

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

Publications

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
Can We Learn Communication-Efficient Optimizers?
Charles-Étienne Joseph
Benjamin Thérien
Abhinav Moudgil
Boris Knyazev
Channel Selection for Test-Time Adaptation Under Distribution Shift
Pedro Vianna
Muawiz Sajjad Chaudhary
An Tang
Guy Cloutier
Michael Eickenberg
To ensure robustness and generalization to real-world scenarios, test-time adaptation has been recently studied as an approach to adjust mod… (see more)els 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 by recalculating batch normalization statistics on test batches. However, in many practical applications this technique is vulnerable to label distribution shifts. We propose to tackle this challenge by only selectively adapting channels in a deep network, minimizing drastic adaptation that is sensitive to label shifts. We find that adapted models significantly improve the performance compared to the baseline models and counteract unknown label shifts.
Learning Optimizers for Local SGD
Charles-Étienne Joseph
Benjamin Thérien
Abhinav Moudgil
Boris Knyazev
Gradient Masked Averaging for Federated Learning
Irene Tenison
Sai Aravind Sreeramadas
Vaikkunth Mugunthan
Edouard Oyallon
Federated learning (FL) is an emerging paradigm that permits a large number of clients with heterogeneous data to coordinate learning of a u… (see more)nified global model without the need to share data amongst each other. A major challenge in federated learning is the heterogeneity of data across client, which can degrade the performance of standard FL algorithms. Standard FL algorithms involve averaging of model parameters or gradient updates to approximate the global model at the server. However, we argue that in heterogeneous settings, averaging can result in information loss and lead to poor generalization due to the bias induced by dominant client gradients. We hypothesize that to generalize better across non-i.i.d datasets, the algorithms should focus on learning the invariant mechanism that is constant while ignoring spurious mechanisms that differ across clients. Inspired from recent works in Out-of-Distribution generalization, we propose a gradient masked averaging approach for FL as an alternative to the standard averaging of client updates. This aggregation technique for client updates can be adapted as a drop-in replacement in most existing federated algorithms. We perform extensive experiments on multiple FL algorithms with in-distribution, real-world, feature-skewed out-of-distribution, and quantity imbalanced datasets and show that it provides consistent improvements, particularly in the case of heterogeneous clients.
DragD3D: Vertex-based Editing for Realistic Mesh Deformations using 2D Diffusion Priors
Tianhao Xie
Sudhir Mudur
Tiberiu Popa
Direct mesh editing and deformation are key components in the geometric modeling and animation pipeline. Direct mesh editing methods are typ… (see more)ically framed as optimization problems combining user-specified vertex constraints with a regularizer that determines the position of the rest of the vertices. The choice of the regularizer is key to the realism and authenticity of the final result. Physics and geometry-based regularizers are not aware of the global context and semantics of the object, and the more recent deep learning priors are limited to a specific class of 3D object deformations. In this work, our main contribution is a local mesh editing method called DragD3D for global context-aware realistic deformation through direct manipulation of a few vertices. DragD3D is not restricted to any class of objects. It achieves this by combining the classic geometric ARAP (as rigid as possible) regularizer with 2D priors obtained from a large-scale diffusion model. Specifically, we render the objects from multiple viewpoints through a differentiable renderer and use the recently introduced DDS loss which scores the faithfulness of the rendered image to one from a diffusion model. DragD3D combines the approximate gradients of the DDS with gradients from the ARAP loss to modify the mesh vertices via neural Jacobian field, while also satisfying vertex constraints. We show that our deformations are realistic and aware of the global context of the objects, and provide better results than just using geometric regularizers.
Comparison of Radiologists and Deep Learning for US Grading of Hepatic Steatosis.
Pedro Vianna
Sara-Ivana Calce
Pamela Boustros
Cassandra Larocque-Rigney
Laurent Patry-Beaudoin
Yi Hui Luo
Emre Aslan
John Marinos
Talal M. Alamri
Kim-Nhien Vu
Jessica Murphy-Lavallée
Jean-Sébastien Billiard
Emmanuel Montagnon
Hongliang Li
Samuel Kadoury
Bich Nguyen
Shanel Gauthier
Benjamin Thérien
Michaël Chassé
Guy Cloutier
An Tang
Background Screening for nonalcoholic fatty liver disease (NAFLD) is suboptimal due to the subjective interpretation of US images. Purpose T… (see more)o evaluate the agreement and diagnostic performance of radiologists and a deep learning model in grading hepatic steatosis in NAFLD at US, with biopsy as the reference standard. Materials and Methods This retrospective study included patients with NAFLD and control patients without hepatic steatosis who underwent abdominal US and contemporaneous liver biopsy from September 2010 to October 2019. Six readers visually graded steatosis on US images twice, 2 weeks apart. Reader agreement was assessed with use of κ statistics. Three deep learning techniques applied to B-mode US images were used to classify dichotomized steatosis grades. Classification performance of human radiologists and the deep learning model for dichotomized steatosis grades (S0, S1, S2, and S3) was assessed with area under the receiver operating characteristic curve (AUC) on a separate test set. Results The study included 199 patients (mean age, 53 years ± 13 [SD]; 101 men). On the test set (n = 52), radiologists had fair interreader agreement (0.34 [95% CI: 0.31, 0.37]) for classifying steatosis grades S0 versus S1 or higher, while AUCs were between 0.49 and 0.84 for radiologists and 0.85 (95% CI: 0.83, 0.87) for the deep learning model. For S0 or S1 versus S2 or S3, radiologists had fair interreader agreement (0.30 [95% CI: 0.27, 0.33]), while AUCs were between 0.57 and 0.76 for radiologists and 0.73 (95% CI: 0.71, 0.75) for the deep learning model. For S2 or lower versus S3, radiologists had fair interreader agreement (0.37 [95% CI: 0.33, 0.40]), while AUCs were between 0.52 and 0.81 for radiologists and 0.67 (95% CI: 0.64, 0.69) for the deep learning model. Conclusion Deep learning approaches applied to B-mode US images provided comparable performance with human readers for detection and grading of hepatic steatosis. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Tuthill in this issue.