| Three scenarios for continual learning GM van de Ven, AS Tolias arXiv preprint arXiv:1904.07734, 2019 | 931 | 2019 |
| Brain-inspired replay for continual learning with artificial neural networks GM van de Ven, HT Siegelmann, AS Tolias Nature Communications 11, 4069, 2020 | 462 | 2020 |
| Hippocampal offline reactivation consolidates recently formed cell assembly patterns during sharp wave-ripples GM van de Ven, S Trouche, CG McNamara, K Allen, D Dupret Neuron 92 (5), 968-974, 2016 | 296 | 2016 |
| Three types of incremental learning GM van de Ven, T Tuytelaars, AS Tolias Nature Machine Intelligence 4 (12), 1185-1197, 2022 | 272 | 2022 |
| Generative replay with feedback connections as a general strategy for continual learning GM van de Ven, AS Tolias arXiv preprint arXiv:1809.10635, 2018 | 239 | 2018 |
| Avalanche: an end-to-end library for continual learning V Lomonaco, L Pellegrini, A Cossu, A Carta, G Graffieti, TL Hayes, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 198 | 2021 |
| Biological underpinnings for lifelong learning machines D Kudithipudi, M Aguilar-Simon, J Babb, M Bazhenov, D Blackiston, ... Nature Machine Intelligence 4 (3), 196-210, 2022 | 175 | 2022 |
| Parsing hippocampal theta oscillations by nested spectral components during spatial exploration and memory-guided behavior V Lopes-dos-Santos, GM van de Ven, A Morley, S Trouche, ... Neuron 100 (4), 940-952, 2018 | 156 | 2018 |
| Recoding a cocaine-place memory engram to a neutral engram in the hippocampus S Trouche, PV Perestenko, GM van de Ven, CT Bratley, CG McNamara, ... Nature Neuroscience 19 (4), 564-567, 2016 | 137 | 2016 |
| Attention fine-tunes auditory–motor processing of speech sounds R Möttönen, GM van de Ven, KE Watkins Journal of Neuroscience 34 (11), 4064-4069, 2014 | 77 | 2014 |
| Class-incremental learning with generative classifiers GM van de Ven, Z Li, AS Tolias Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 55 | 2021 |
| Energy-based models for continual learning S Li, Y Du, GM van de Ven, I Mordatch Conference on Lifelong Learning Agents (CoLLAs) – oral, 2022 | 45* | 2022 |
| Natural continual learning: success is a journey, not (just) a destination TC Kao*, KT Jensen*, GM van de Ven, A Bernacchia, G Hennequin Advances in Neural Information Processing Systems (NeurIPS) 34, 2021 | 43 | 2021 |
| Continual evaluation for lifelong learning: identifying the stability gap M De Lange, GM van de Ven, T Tuytelaars International Conference on Learning Representations (ICLR) – spotlight, 2023 | 32 | 2023 |
| Three continual learning scenarios GM van de Ven, AS Tolias NeurIPS, Continual Learning Workshop, 2018 | 29 | 2018 |
| Representation ensembling for synergistic lifelong learning with quasilinear complexity JT Vogelstein*, J Dey*, HS Helm, W LeVine, RD Mehta, TM Tomita, H Xu, ... arXiv preprint arXiv:2004.12908, 2020 | 20* | 2020 |
| Continual learning: applications and the road forward E Verwimp, R Aljundi, S Ben-David, M Bethge, A Cossu, A Gepperth, ... Transactions on Machine Learning Research (TMLR), 2024 | 10 | 2024 |
| Three continual learning scenarios and a case for generative replay GM van de Ven, AS Tolias Submission to ICLR, 2018 | 9 | 2018 |
| Prediction error-based classification for class-incremental learning M Zając, T Tuytelaars, GM van de Ven International Conference on Learning Representations (ICLR), 2024 | 5 | 2024 |
| Knowledge accumulation in continually learned representations and the issue of feature forgetting T Hess*, E Verwimp*, GM van de Ven, T Tuytelaars Transactions on Machine Learning Research (TMLR), 2024 | 4 | 2024 |
Andreas ToliasStanford University在 stanford.edu 的电子邮件经过验证
