Online Continual Learning with Maximally Interfered Retrieval R Aljundi, L Caccia, E Belilovsky, M Caccia, M Lin, L Charlin, T Tuytelaars Advances In neural Information Processing Systems (NeurIPS), 2019 | 538 | 2019 |
CLIP-Mesh: Generating textured meshes from text using pretrained image-text models N Mohammad Khalid, T Xie, E Belilovsky, T Popa SIGGRAPH Asia 2022 Conference Papers, 1-8, 2022 | 223 | 2022 |
Greedy Layerwise Learning Can Scale to ImageNet E Belilovsky, M Eickenberg, E Oyallon Proceedings of the 36th International Conference on Machine Learning (ICML …, 2019 | 196 | 2019 |
Scaling the Scattering Transform: Deep Hybrid Networks E Oyallon, E Belilovsky, S Zagoruyko International Conference on Computer Vision (ICCV), 5618-5627, 2017 | 193 | 2017 |
Kymatio: Scattering transforms in python M Andreux, T Angles, G Exarchakis, R Leonarduzzi, G Rochette, L Thiry, ... Journal of Machine Learning Research 21 (60), 1-6, 2020 | 174 | 2020 |
New Insights on Reducing Abrupt Representation Change in Online Continual Learning L Caccia, R Aljundi, N Asadi, T Tuytelaars, J Pineau, E Belilovsky International Conference on Learning Representations (ICLR), 2022 | 153 | 2022 |
Decoupled greedy learning of cnns E Belilovsky, M Eickenberg, E Oyallon Proceedings of the 37th International Conference on Machine Learning (ICML …, 2019 | 113 | 2019 |
Scattering networks for hybrid representation learning E Oyallon, S Zagoruyko, G Huang, N Komodakis, S Lacoste-Julien, ... IEEE transactions on pattern analysis and machine intelligence 41 (9), 2208-2221, 2018 | 96 | 2018 |
A Test of Relative Similarity for Model Selection in Generative Models E Belilovsky, W Bounliphone, MB Blaschko, I Antonoglou, A Gretton International Conference on Learning Representations (ICLR), 2016 | 83* | 2016 |
Online Learned Continual Compression with Adaptive Quantization Modules L Caccia, E Belilovsky, M Caccia, J Pineau International Conference on Machine Learning (ICML), 2020 | 78 | 2020 |
Probing Representation Forgetting in Supervised and Unsupervised Continual Learning MR Davari, N Asadi, S Mudur, R Aljundi, E Belilovsky IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022 | 77 | 2022 |
Testing for Differences in Gaussian Graphical Models: Applications to Brain Connectivity E Belilovsky, G Varoquaux, MB Blaschko Advances In neural Information Processing Systems (NIPS), 2016 | 69 | 2016 |
Blindfold Baselines for Embodied QA A Anand, E Belilovsky, K Kastner, H Larochelle, A Courville NIPS VIGIL Workshop arXiv preprint arXiv:1811.05013, 2018 | 51 | 2018 |
Graph Density-Aware Losses for Novel Compositions in Scene Graph Generation B Knyazev, H de Vries, C Cangea, GW Taylor, A Courville, E Belilovsky British Machine Vision Conference (BMVC), 2020 | 42 | 2020 |
Compressing the Input for CNNs with the First-Order Scattering Transform E Oyallon, E Belilovsky, S Zagoruyko, M Valko European Conference on Computer Vision (ECCV), 2018 | 40 | 2018 |
Learning to Discover Sparse Graphical Models E Belilovsky, K Kastner, G Varoquaux, MB Blaschko International Conference on Machine Learning (ICML), 2017 | 36 | 2017 |
Continual Pre-Training of Large Language Models: How to (re) warm your model? K Gupta, B Thérien, A Ibrahim, ML Richter, Q Anthony, E Belilovsky, I Rish, ... ICML Workshop on Efficient Foundational Models 2023, 2023 | 34 | 2023 |
Graph density-aware losses for novel compositions in scene graph generation EB Knyazev, Boris, Harm de Vries, Catalina Cangea, Graham W Taylor, Aaron ... arXiv preprint arXiv:2005.08230 2 (3), 2020 | 31* | 2020 |
Generative Compositional Augmentations for Scene Graph Prediction B Knyazev, H de Vries, C Cangea, GW Taylor, A Courville, E Belilovsky International Conference on Computer Vision (ICCV) arXiv preprint arXiv:2007 …, 2021 | 28* | 2021 |
Revisiting learnable affines for batch norm in few-shot transfer learning M Yazdanpanah, AA Rahman, M Chaudhary, C Desrosiers, M Havaei, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 25 | 2022 |