| Learning to remember: A synaptic plasticity driven framework for continual learning O Ostapenko, M Puscas, T Klein, P Jahnichen, M Nabi Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 364 | 2019 |
| Online fast adaptation and knowledge accumulation (osaka): a new approach to continual learning M Caccia, P Rodriguez, O Ostapenko, F Normandin, M Lin, ... Advances in Neural Information Processing Systems 33, 16532-16545, 2020 | 172* | 2020 |
| Continual learning via local module composition O Ostapenko, P Rodriguez, M Caccia, L Charlin Advances in Neural Information Processing Systems 34, 30298-30312, 2021 | 82 | 2021 |
| Continual Learning with Foundational Models: An Empirical Study of Latent Replay O Ostapenko, T Lesort, P Rodríguez, MR Arefin, A Douillard, I Rish, ... CoLLAs 2022, 2022 | 77* | 2022 |
| Towards modular llms by building and reusing a library of loras O Ostapenko, Z Su, EM Ponti, L Charlin, NL Roux, M Pereira, L Caccia, ... ICML 2024, 2024 | 27 | 2024 |
| Sequoia: A software framework to unify continual learning research F Normandin, F Golemo, O Ostapenko, P Rodriguez, MD Riemer, ... arXiv preprint arXiv:2108.01005, 2021 | 24 | 2021 |
| Self-paced adversarial training for multimodal few-shot learning F Pahde, O Ostapenko, PJ Hnichen, T Klein, M Nabi 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), 218-226, 2019 | 20 | 2019 |
| Pruning at a glance: Global neural pruning for model compression A Salama, O Ostapenko, T Klein, M Nabi arXiv preprint arXiv:1912.00200, 2019 | 16 | 2019 |
| Prune your neurons blindly: Neural network compression through structured class-blind pruning A Salama, O Ostapenko, T Klein, M Nabi ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and …, 2019 | 14 | 2019 |
| Learning to remember: A synaptic plasticity driven framework for continual learning. 2019 IEEE O Ostapenko, MM Puscas, T Klein, P Jähnichen, M Nabi CVF Conference on Computer Vision and Pattern Recognition (CVPR) 10, 2019 | 13 | 2019 |
| Guiding language model reasoning with planning tokens X Wang, L Caccia, O Ostapenko, X Yuan, WY Wang, A Sordoni COLM 2024, 2023 | 10 | 2023 |
| A case study of instruction tuning with mixture of parameter-efficient experts O Ostapenko, L Caccia, Z Su, N Le Roux, L Charlin, A Sordoni NeurIPS 2023 Workshop on Instruction Tuning and Instruction Following, 2023 | 9 | 2023 |
| Scaling the number of tasks in continual learning T Lesort, O Ostapenko, D Misra, MR Arefin, P Rodríguez, L Charlin, I Rish arXiv preprint arXiv:2207.04543 2, 2022 | 9 | 2022 |
| Challenging common assumptions about catastrophic forgetting and knowledge accumulation T Lesort, O Ostapenko, P Rodríguez, D Misra, MR Arefin, L Charlin, I Rish Conference on Lifelong Learning Agents, 43-65, 2023 | 8 | 2023 |
| Challenging common assumptions about catastrophic forgetting T Lesort, O Ostapenko, D Misra, MR Arefin, P Rodríguez, L Charlin, I Rish arXiv preprint arXiv:2207.04543, 2022 | 5 | 2022 |
| Attention for Compositional Modularity O Ostapenko, P Rodriguez, A Lacoste, L Charlin NeurIPS'22 Workshop on All Things Attention: Bridging Different Perspectives …, 2022 | 4 | 2022 |
| Self-paced adversarial training for multimodal and 3D model few-shot learning F Pahde, O Ostapenko, T Klein, M Nabi, M Puscas US Patent 10,990,848, 2021 | 4 | 2021 |
| Online fast adaptation and knowledge accumulation: a new approach to continual learning. March 2020 M Caccia, P Rodriguez, O Ostapenko, F Normandin, M Lin, L Caccia, ... URL http://arxiv. org/abs, 2003 | 4 | 2003 |
| Sequoia-towards a systematic organization of continual learning research F Normandin, F Golemo, O Ostapenko, M Riemer, P Rodriguez, J Hurtado, ... Github repository, 2021 | 3 | 2021 |
| Pruning at a glance: A structured class-blind pruning technique for model compression A Salama, O Ostapenko, M Nabi, T Klein | 3 | 2018 |
Laurent CharlinAssociate Professor, HEC Montréal & Mila, Canada CIFAR AI Chairبريد إلكتروني تم التحقق منه على hec.ca