| 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 | 586 | 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 | 245 | 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 | 207 | 2019 |
| Scaling the Scattering Transform: Deep Hybrid Networks E Oyallon, E Belilovsky, S Zagoruyko International Conference on Computer Vision (ICCV), 5618-5627, 2017 | 203 | 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 | 187 | 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 | 183 | 2022 |
| Decoupled greedy learning of cnns E Belilovsky, M Eickenberg, E Oyallon Proceedings of the 37th International Conference on Machine Learning (ICML …, 2019 | 121 | 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 | 102 | 2018 |
| 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 | 93 | 2022 |
| Online Learned Continual Compression with Adaptive Quantization Modules L Caccia, E Belilovsky, M Caccia, J Pineau International Conference on Machine Learning (ICML), 2020 | 89 | 2020 |
| 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 | 86* | 2016 |
| 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 | 73 | 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 | 52 | 2018 |
| 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 | 51 | 2023 |
| 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 | 43 | 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 | 41 | 2018 |
| Learning to Discover Sparse Graphical Models E Belilovsky, K Kastner, G Varoquaux, MB Blaschko International Conference on Machine Learning (ICML), 2017 | 37 | 2017 |
| Reliability of CKA as a Similarity Measure in Deep Learning MR Davari, S Horoi, A Natik, G Lajoie, G Wolf, E Belilovsky International Conference on Learning Representations (ICLR) 2023, 2022 | 31 | 2022 |
| 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 |
| Simple and scalable strategies to continually pre-train large language models A Ibrahim, B Thérien, K Gupta, ML Richter, Q Anthony, T Lesort, ... arXiv preprint arXiv:2403.08763, 2024 | 30 | 2024 |
Edouard OyallonCNRS, Sorbonne UniversityVerified email at cnrs.fr
