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ORCIDEugene Belilovsky
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2020 – today
- 2024
[j6]Adam Ibrahim, Benjamin Thérien, Kshitij Gupta, Mats L. Richter, Quentin Gregory Anthony, Eugene Belilovsky, Timothée Lesort, Irina Rish:
Simple and Scalable Strategies to Continually Pre-train Large Language Models. Trans. Mach. Learn. Res. 2024 (2024)- [c33]Géraldin Nanfack, Alexander Fulleringer, Jonathan Marty, Michael Eickenberg, Eugene Belilovsky:
Adversarial Attacks on the Interpretation of Neuron Activation Maximization. AAAI 2024: 4315-4324 - [c32]Stefan Horoi, Albert Manuel Orozco Camacho, Eugene Belilovsky, Guy Wolf:
Harmony in Diversity: Merging Neural Networks with Canonical Correlation Analysis. ICML 2024 - [i53]Pedro Vianna, Muawiz Chaudhary, Paria Mehrbod, An Tang, Guy Cloutier, Guy Wolf, Michael Eickenberg, Eugene Belilovsky:
Channel-Selective Normalization for Label-Shift Robust Test-Time Adaptation. CoRR abs/2402.04958 (2024) - [i52]Adam Ibrahim, Benjamin Thérien, Kshitij Gupta, Mats L. Richter, Quentin Anthony, Timothée Lesort, Eugene Belilovsky, Irina Rish:
Simple and Scalable Strategies to Continually Pre-train Large Language Models. CoRR abs/2403.08763 (2024) - [i51]Damien Martins Gomes, Yanlei Zhang, Eugene Belilovsky, Guy Wolf, Mahdi S. Hosseini:
AdaFisher: Adaptive Second Order Optimization via Fisher Information. CoRR abs/2405.16397 (2024) - [i50]Louis Fournier, Adel Nabli, Masih Aminbeidokhti, Marco Pedersoli, Eugene Belilovsky, Edouard Oyallon:
WASH: Train your Ensemble with Communication-Efficient Weight Shuffling, then Average. CoRR abs/2405.17517 (2024) - [i49]Benjamin Thérien, Charles-Étienne Joseph, Boris Knyazev, Edouard Oyallon, Irina Rish, Eugene Belilovsky:
μLO: Compute-Efficient Meta-Generalization of Learned Optimizers. CoRR abs/2406.00153 (2024) - [i48]AmirHossein Zamani, Amir G. Aghdam, Tiberiu Popa, Eugene Belilovsky:
Temporally Consistent Object Editing in Videos using Extended Attention. CoRR abs/2406.00272 (2024) - [i47]Géraldin Nanfack, Michael Eickenberg, Eugene Belilovsky:
From Feature Visualization to Visual Circuits: Effect of Adversarial Model Manipulation. CoRR abs/2406.01365 (2024) - [i46]Stéphane Rivaud, Louis Fournier, Thomas Pumir, Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon:
PETRA: Parallel End-to-end Training with Reversible Architectures. CoRR abs/2406.02052 (2024) - [i45]Adel Nabli, Louis Fournier, Pierre Erbacher, Louis Serrano, Eugene Belilovsky, Edouard Oyallon:
ACCO: Accumulate while you Communicate, Hiding Communications in Distributed LLM Training. CoRR abs/2406.02613 (2024) - [i44]Vaibhav Singh, Rahaf Aljundi, Eugene Belilovsky:
Controlling Forgetting with Test-Time Data in Continual Learning. CoRR abs/2406.13653 (2024) - [i43]Stefan Horoi, Albert Manuel Orozco Camacho, Eugene Belilovsky, Guy Wolf:
Harmony in Diversity: Merging Neural Networks with Canonical Correlation Analysis. CoRR abs/2407.05385 (2024) - [i42]Boris Knyazev, Abhinav Moudgil, Guillaume Lajoie, Eugene Belilovsky, Simon Lacoste-Julien:
Accelerating Training with Neuron Interaction and Nowcasting Networks. CoRR abs/2409.04434 (2024) - [i41]Humza Wajid Hameed, Géraldin Nanfack, Eugene Belilovsky:
Not Only the Last-Layer Features for Spurious Correlations: All Layer Deep Feature Reweighting. CoRR abs/2409.14637 (2024) - 2023
- [j5]Irene Tenison, Sai Aravind Sreeramadas, Vaikkunth Mugunthan, Edouard Oyallon, Irina Rish, Eugene Belilovsky:
Gradient Masked Averaging for Federated Learning. Trans. Mach. Learn. Res. 2023 (2023) - [c31]Gwen Legate, Lucas Caccia, Eugene Belilovsky:
Re-Weighted Softmax Cross-Entropy to Control Forgetting in Federated Learning. CoLLAs 2023: 764-780 - [c30]AmirMohammad Sarfi, Zahra Karimpour, Muawiz Chaudhary, Nasir Mohammad Khalid, Mirco Ravanelli, Sudhir Mudur, Eugene Belilovsky:
Simulated Annealing in Early Layers Leads to Better Generalization. CVPR 2023: 20205-20214 - [c29]MohammadReza Davari, Stefan Horoi, Amine Natik, Guillaume Lajoie, Guy Wolf, Eugene Belilovsky:
Reliability of CKA as a Similarity Measure in Deep Learning. ICLR 2023 - [c28]Nader Asadi, MohammadReza Davari, Sudhir Mudur, Rahaf Aljundi, Eugene Belilovsky:
Prototype-Sample Relation Distillation: Towards Replay-Free Continual Learning. ICML 2023: 1093-1106 - [c27]Louis Fournier, Stéphane Rivaud, Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon:
Can Forward Gradient Match Backpropagation? ICML 2023: 10249-10264 - [c26]Gwen Legate, Nicolas Bernier, Lucas Page-Caccia, Edouard Oyallon, Eugene Belilovsky:
Guiding The Last Layer in Federated Learning with Pre-Trained Models. NeurIPS 2023 - [c25]Adel Nabli, Eugene Belilovsky, Edouard Oyallon:
A2CiD2: Accelerating Asynchronous Communication in Decentralized Deep Learning. NeurIPS 2023 - [i40]Adeetya Patel, Michael Eickenberg, Eugene Belilovsky:
Local Learning with Neuron Groups. CoRR abs/2301.07635 (2023) - [i39]Medric Sonwa, Johanna Hansen, Eugene Belilovsky:
Imitation from Observation With Bootstrapped Contrastive Learning. CoRR abs/2302.06540 (2023) - [i38]Nader Asadi, MohammadReza Davari, Sudhir Mudur, Rahaf Aljundi, Eugene Belilovsky:
Prototype-Sample Relation Distillation: Towards Replay-Free Continual Learning. CoRR abs/2303.14771 (2023) - [i37]AmirMohammad Sarfi, Zahra Karimpour, Muawiz Chaudhary, Nasir Mohammad Khalid, Mirco Ravanelli, Sudhir Mudur, Eugene Belilovsky:
Simulated Annealing in Early Layers Leads to Better Generalization. CoRR abs/2304.04858 (2023) - [i36]Gwen Legate, Lucas Caccia, Eugene Belilovsky:
Re-Weighted Softmax Cross-Entropy to Control Forgetting in Federated Learning. CoRR abs/2304.05260 (2023) - [i35]Gwen Legate, Nicolas Bernier, Lucas Caccia, Edouard Oyallon, Eugene Belilovsky:
Guiding The Last Layer in Federated Learning with Pre-Trained Models. CoRR abs/2306.03937 (2023) - [i34]Louis Fournier, Stéphane Rivaud, Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon:
Can Forward Gradient Match Backpropagation? CoRR abs/2306.06968 (2023) - [i33]Géraldin Nanfack, Alexander Fulleringer, Jonathan Marty, Michael Eickenberg, Eugene Belilovsky:
Adversarial Attacks on the Interpretation of Neuron Activation Maximization. CoRR abs/2306.07397 (2023) - [i32]Adel Nabli, Eugene Belilovsky, Edouard Oyallon:
A2CiD2: Accelerating Asynchronous Communication in Decentralized Deep Learning. CoRR abs/2306.08289 (2023) - [i31]Kshitij Gupta, Benjamin Thérien, Adam Ibrahim, Mats L. Richter, Quentin Anthony, Eugene Belilovsky, Irina Rish, Timothée Lesort:
Continual Pre-Training of Large Language Models: How to (re)warm your model? CoRR abs/2308.04014 (2023) - [i30]Tianhao Xie, Eugene Belilovsky, Sudhir Mudur, Tiberiu Popa:
DragD3D: Vertex-based Editing for Realistic Mesh Deformations using 2D Diffusion Priors. CoRR abs/2310.04561 (2023) - [i29]Charles-Étienne Joseph, Benjamin Thérien, Abhinav Moudgil, Boris Knyazev, Eugene Belilovsky:
Can We Learn Communication-Efficient Optimizers? CoRR abs/2312.02204 (2023) - [i28]MohammadReza Davari, Eugene Belilovsky:
Model Breadcrumbs: Scaling Multi-Task Model Merging with Sparse Masks. CoRR abs/2312.06795 (2023) - 2022
- [c24]Shanel Gauthier, Benjamin Thérien, Laurent Alsène-Racicot, Muawiz Chaudhary, Irina Rish, Eugene Belilovsky, Michael Eickenberg, Guy Wolf:
Parametric Scattering Networks. CVPR 2022: 5739-5748 - [c23]Moslem Yazdanpanah, Aamer Abdul Rahman, Muawiz Chaudhary, Christian Desrosiers, Mohammad Havaei, Eugene Belilovsky, Samira Ebrahimi Kahou:
Revisiting Learnable Affines for Batch Norm in Few-Shot Transfer Learning. CVPR 2022: 9099-9108 - [c22]MohammadReza Davari, Nader Asadi, Sudhir Mudur, Rahaf Aljundi, Eugene Belilovsky:
Probing Representation Forgetting in Supervised and Unsupervised Continual Learning. CVPR 2022: 16691-16700 - [c21]Lucas Caccia, Rahaf Aljundi, Nader Asadi, Tinne Tuytelaars, Joelle Pineau, Eugene Belilovsky:
New Insights on Reducing Abrupt Representation Change in Online Continual Learning. ICLR 2022 - [c20]Maxence Ernoult, Fabrice Normandin, Abhinav Moudgil, Sean Spinney, Eugene Belilovsky, Irina Rish, Blake A. Richards, Yoshua Bengio:
Towards Scaling Difference Target Propagation by Learning Backprop Targets. ICML 2022: 5968-5987 - [c19]Nasir Mohammad Khalid, Tianhao Xie, Eugene Belilovsky
, Tiberiu Popa:
CLIP-Mesh: Generating textured meshes from text using pretrained image-text models. SIGGRAPH Asia 2022: 25:1-25:8 - [i27]Irene Tenison, Sai Aravind Sreeramadas, Vaikkunth Mugunthan, Edouard Oyallon, Eugene Belilovsky, Irina Rish:
Gradient Masked Averaging for Federated Learning. CoRR abs/2201.11986 (2022) - [i26]Maxence Ernoult, Fabrice Normandin, Abhinav Moudgil, Sean Spinney, Eugene Belilovsky, Irina Rish, Blake A. Richards, Yoshua Bengio:
Towards Scaling Difference Target Propagation by Learning Backprop Targets. CoRR abs/2201.13415 (2022) - [i25]Nader Asadi, Sudhir Mudur, Eugene Belilovsky:
Tackling Online One-Class Incremental Learning by Removing Negative Contrasts. CoRR abs/2203.13307 (2022) - [i24]Nasir Mohammad Khalid, Tianhao Xie, Eugene Belilovsky, Tiberiu Popa:
Text to Mesh Without 3D Supervision Using Limit Subdivision. CoRR abs/2203.13333 (2022) - [i23]MohammadReza Davari, Nader Asadi, Sudhir Mudur, Rahaf Aljundi, Eugene Belilovsky:
Probing Representation Forgetting in Supervised and Unsupervised Continual Learning. CoRR abs/2203.13381 (2022) - [i22]MohammadReza Davari, Stefan Horoi, Amine Natik, Guillaume Lajoie, Guy Wolf, Eugene Belilovsky:
Reliability of CKA as a Similarity Measure in Deep Learning. CoRR abs/2210.16156 (2022) - 2021
- [c18]Boris Knyazev, Harm de Vries, Catalina Cangea, Graham W. Taylor, Aaron C. Courville, Eugene Belilovsky:
Generative Compositional Augmentations for Scene Graph Prediction. ICCV 2021: 15807-15817 - [c17]Louis Thiry, Michael Arbel, Eugene Belilovsky, Edouard Oyallon:
The Unreasonable Effectiveness of Patches in Deep Convolutional Kernels Methods. ICLR 2021 - [i21]Louis Thiry, Michael Arbel, Eugene Belilovsky, Edouard Oyallon:
The Unreasonable Effectiveness of Patches in Deep Convolutional Kernels Methods. CoRR abs/2101.07528 (2021) - [i20]Lucas Caccia, Rahaf Aljundi, Tinne Tuytelaars, Joelle Pineau, Eugene Belilovsky:
Reducing Representation Drift in Online Continual Learning. CoRR abs/2104.05025 (2021) - [i19]Eugene Belilovsky, Louis Leconte, Lucas Caccia, Michael Eickenberg, Edouard Oyallon:
Decoupled Greedy Learning of CNNs for Synchronous and Asynchronous Distributed Learning. CoRR abs/2106.06401 (2021) - [i18]Mateusz Michalkiewicz, Stavros Tsogkas, Sarah Parisot, Mahsa Baktashmotlagh, Anders P. Eriksson, Eugene Belilovsky:
Learning Compositional Shape Priors for Few-Shot 3D Reconstruction. CoRR abs/2106.06440 (2021) - [i17]Shanel Gauthier, Benjamin Thérien, Laurent Alsène-Racicot, Irina Rish, Eugene Belilovsky, Michael Eickenberg, Guy Wolf:
Parametric Scattering Networks. CoRR abs/2107.09539 (2021) - 2020
- [j4]Mathieu Andreux, Tomás Angles, Georgios Exarchakis, Roberto Leonarduzzi, Gaspar Rochette, Louis Thiry, John Zarka, Stéphane Mallat, Joakim Andén, Eugene Belilovsky, Joan Bruna, Vincent Lostanlen, Muawiz Chaudhary, Matthew J. Hirn, Edouard Oyallon, Sixin Zhang, Carmine-Emanuele Cella, Michael Eickenberg:
Kymatio: Scattering Transforms in Python. J. Mach. Learn. Res. 21: 60:1-60:6 (2020) - [c16]Boris Knyazev, Harm de Vries, Catalina Cangea, Graham W. Taylor, Aaron C. Courville, Eugene Belilovsky:
Graph Density-Aware Losses for Novel Compositions in Scene Graph Generation. BMVC 2020 - [c15]Mateusz Michalkiewicz, Eugene Belilovsky, Mahsa Baktashmotlagh, Anders P. Eriksson:
A Simple and Scalable Shape Representation for 3D Reconstruction. BMVC 2020 - [c14]Mateusz Michalkiewicz, Sarah Parisot, Stavros Tsogkas, Mahsa Baktashmotlagh, Anders P. Eriksson, Eugene Belilovsky:
Few-Shot Single-View 3-D Object Reconstruction with Compositional Priors. ECCV (25) 2020: 614-630 - [c13]Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon:
Decoupled Greedy Learning of CNNs. ICML 2020: 736-745 - [c12]Lucas Caccia, Eugene Belilovsky, Massimo Caccia, Joelle Pineau:
Online Learned Continual Compression with Adaptive Quantization Modules. ICML 2020: 1240-1250 - [i16]Mateusz Michalkiewicz, Sarah Parisot, Stavros Tsogkas, Mahsa Baktashmotlagh, Anders P. Eriksson, Eugene Belilovsky:
Few-Shot Single-View 3-D Object Reconstruction with Compositional Priors. CoRR abs/2004.06302 (2020) - [i15]Mateusz Michalkiewicz, Eugene Belilovsky, Mahsa Baktashmotlagh, Anders P. Eriksson:
A Simple and Scalable Shape Representation for 3D Reconstruction. CoRR abs/2005.04623 (2020) - [i14]Boris Knyazev, Harm de Vries, Catalina Cangea, Graham W. Taylor, Aaron C. Courville, Eugene Belilovsky:
Graph Density-Aware Losses for Novel Compositions in Scene Graph Generation. CoRR abs/2005.08230 (2020) - [i13]Boris Knyazev, Harm de Vries, Catalina Cangea, Graham W. Taylor, Aaron C. Courville, Eugene Belilovsky:
Generative Graph Perturbations for Scene Graph Prediction. CoRR abs/2007.05756 (2020)
2010 – 2019
- 2019
- [j3]Edouard Oyallon, Sergey Zagoruyko, Gabriel Huang, Nikos Komodakis, Simon Lacoste-Julien, Matthew B. Blaschko, Eugene Belilovsky:
Scattering Networks for Hybrid Representation Learning. IEEE Trans. Pattern Anal. Mach. Intell. 41(9): 2208-2221 (2019) - [c11]Catalina Cangea, Eugene Belilovsky, Aaron C. Courville:
VideoNavQA: Bridging the Gap between Visual and Embodied Question Answering. BMVC 2019: 280 - [c10]Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon:
Greedy Layerwise Learning Can Scale To ImageNet. ICML 2019: 583-593 - [c9]Rahaf Aljundi, Eugene Belilovsky, Tinne Tuytelaars, Laurent Charlin, Massimo Caccia, Min Lin, Lucas Page-Caccia:
Online Continual Learning with Maximal Interfered Retrieval. NeurIPS 2019: 11849-11860 - [i12]Catalina Cangea, Eugene Belilovsky, Pietro Liò, Aaron C. Courville:
VideoNavQA: Bridging the Gap between Visual and Embodied Question Answering. ViGIL@NeurIPS 2019 - [i11]Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon:
Decoupled Greedy Learning of CNNs. CoRR abs/1901.08164 (2019) - [i10]Rahaf Aljundi, Lucas Caccia, Eugene Belilovsky, Massimo Caccia, Min Lin, Laurent Charlin, Tinne Tuytelaars:
Online Continual Learning with Maximally Interfered Retrieval. CoRR abs/1908.04742 (2019) - [i9]Catalina Cangea, Eugene Belilovsky, Pietro Liò, Aaron C. Courville:
VideoNavQA: Bridging the Gap between Visual and Embodied Question Answering. CoRR abs/1908.04950 (2019) - [i8]Lucas Caccia, Eugene Belilovsky, Massimo Caccia, Joelle Pineau:
Online Learned Continual Compression with Stacked Quantization Module. CoRR abs/1911.08019 (2019) - 2018
- [b1]Eugene Belilovsky:
Structured Sparse Learning on Graphs in High-Dimensional Data with Applications to NeuroImaging. (Apprentissage de graphes structuré et parcimonieux dans des données de haute dimension avec applications à l'imagerie cérébrale). CentraleSupélec, Châtenay-Malabry, France, 2018 - [c8]Edouard Oyallon, Eugene Belilovsky, Sergey Zagoruyko, Michal Valko:
Compressing the Input for CNNs with the First-Order Scattering Transform. ECCV (9) 2018: 305-320 - [i7]Edouard Oyallon, Sergey Zagoruyko, Gabriel Huang, Nikos Komodakis, Simon Lacoste-Julien, Matthew B. Blaschko, Eugene Belilovsky:
Scattering Networks for Hybrid Representation Learning. CoRR abs/1809.06367 (2018) - [i6]Edouard Oyallon, Eugene Belilovsky, Sergey Zagoruyko, Michal Valko:
Compressing the Input for CNNs with the First-Order Scattering Transform. CoRR abs/1809.10200 (2018) - [i5]Ankesh Anand, Eugene Belilovsky, Kyle Kastner, Hugo Larochelle, Aaron C. Courville:
Blindfold Baselines for Embodied QA. CoRR abs/1811.05013 (2018) - [i4]Mathieu Andreux, Tomás Angles, Georgios Exarchakis, Roberto Leonarduzzi, Gaspar Rochette, Louis Thiry, John Zarka, Stéphane Mallat, Joakim Andén, Eugene Belilovsky, Joan Bruna, Vincent Lostanlen, Matthew J. Hirn, Edouard Oyallon, Sixin Zhang, Carmine-Emanuele Cella, Michael Eickenberg:
Kymatio: Scattering Transforms in Python. CoRR abs/1812.11214 (2018) - [i3]Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon:
Greedy Layerwise Learning Can Scale to ImageNet. CoRR abs/1812.11446 (2018) - 2017
- [c7]Edouard Oyallon, Eugene Belilovsky, Sergey Zagoruyko:
Scaling the Scattering Transform: Deep Hybrid Networks. ICCV 2017: 5619-5628 - [c6]Eugene Belilovsky, Matthew B. Blaschko, Jamie Ryan Kiros, Raquel Urtasun, Richard S. Zemel:
Joint Embeddings of Scene Graphs and Images. ICLR (Workshop) 2017 - [c5]Eugene Belilovsky, Kyle Kastner, Gaël Varoquaux, Matthew B. Blaschko:
Learning to Discover Sparse Graphical Models. ICLR (Workshop) 2017 - [c4]Eugene Belilovsky, Kyle Kastner, Gaël Varoquaux, Matthew B. Blaschko:
Learning to Discover Sparse Graphical Models. ICML 2017: 440-448 - [i2]Edouard Oyallon, Eugene Belilovsky, Sergey Zagoruyko:
Scaling the Scattering Transform: Deep Hybrid Networks. CoRR abs/1703.08961 (2017) - 2016
- [c3]Eugene Belilovsky, Gaël Varoquaux, Matthew B. Blaschko:
Testing for Differences in Gaussian Graphical Models: Applications to Brain Connectivity. NIPS 2016: 595-603 - [c2]Wacha Bounliphone, Eugene Belilovsky, Matthew B. Blaschko, Ioannis Antonoglou, Arthur Gretton:
A Test of Relative Similarity For Model Selection in Generative Models. ICLR (Poster) 2016 - [i1]Wacha Bounliphone, Eugene Belilovsky, Arthur Tenenhaus, Ioannis Antonoglou, Arthur Gretton, Matthew B. Blaschko:
Fast Non-Parametric Tests of Relative Dependency and Similarity. CoRR abs/1611.05740 (2016) - 2015
- [j2]Eugene Belilovsky, Katerina Gkirtzou, Michail Misyrlis, Anna B. Konova, Jean Honorio, Nelly Alia-Klein, Rita Z. Goldstein, Dimitris Samaras, Matthew B. Blaschko:
Predictive sparse modeling of fMRI data for improved classification, regression, and visualization using the k-support norm. Comput. Medical Imaging Graph. 46: 40-46 (2015) - [j1]Eugene Belilovsky, Andreas Argyriou, Gaël Varoquaux, Matthew B. Blaschko:
Convex relaxations of penalties for sparse correlated variables with bounded total variation. Mach. Learn. 100(2-3): 533-553 (2015)
2000 – 2009
- 2009
- [c1]Florian Müller, Eugene Belilovsky, Alfred Mertins:
Generalized cyclic transformations in speaker-independent speech recognition. ASRU 2009: 211-215
Coauthor Index
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