Zając M, Tuytelaars T, van de Ven GM (2024), “Prediction error-based classification for class-incremental learning”, International Conference on Learning Representations (ICLR). [paper] [preprint]

Verwimp E, Aljundi R, Ben-David S, Bethge M, Cossu A, Gepperth A, Hayes TL, Hüllermeier E, Kanan C, Kudithipudi D, Lampert CH, Mundt M, Pascanu R, Popescu A, Tolias AS, van de Weijer J, Liu B, Lomonaco V, Tuytelaars T, van de Ven GM (2024), “Continual learning: applications and the road forward”, Transactions on Machine Learning Research (TMLR). [paper] [preprint]

De Lange M, van de Ven GM, Tuytelaars T (2023), “Continual evaluation for lifelong learning: Identifying the stability gap”, International Conference on Learning Representations (ICLR), spotlight (top 25%). [paper] [preprint] [code]

van de Ven GM, Tuytelaars T, Tolias AS (2022), “Three types of incremental learning”, Nature Machine Intelligence, 4(12): 1185-1197. [paper] [code] [presentation]

Kudithipudi D, Aguilar-Simon M, Babb J, Bazhenov M, Blackiston D, Bongard J, Brna A, Chakravarthi Raja S, Cheney N, Clune J, Daram A, Fusi S, …, van de Ven GM, …, Siegelmann HT (2022), “Biological underpinnings for lifelong learning machines”, Nature Machine Intelligence, 4(3): 196-210. [paper]

Li S, Du Y, van de Ven GM, Mordatch I (2022), “Energy-based models for continual learning”, Proceedings of The 1st Conference on Lifelong Learning Agents (CoLLAs), PMLR 199: 1-22. [paper] [preprint] [code]

van de Ven GM, Zhe L, Tolias AS (2021), “Class-incremental learning with generative classifiers”, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPR-W), p. 3611-3620. [paper] [preprint] [code] [presentation]

Kao TC, Jensen KT, van de Ven GM, Bernacchia A, Hennequin G (2021), “Natural continual learning: success is a journey, not (just) a destination”, Advances in Neural Information Processing Systems (NeurIPS), 34. [paper] [preprint] [code]

Lomonaco V, Pellegrini L, Cossu A, Carta A, Graffieti G, Hayes TL, De Lange M, Masana M, Pomponi J, van de Ven GM, Mundt M, She Q, …, Maltoni D (2021), “Avalanche: an end-to-end library for continual learning”, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPR-W), p. 3600-3610. [paper] [preprint] [website]

van de Ven GM, Siegelmann HT, Tolias AS (2020), “Brain-inspired replay for continual learning with artificial neural networks”, Nature Communications, 11: 4069. [paper] [workshop version] [code] [presentation]

van de Ven GM, Tolias AS (2019), “Three scenarios for continual learning”, NeurIPS workshop on Continual Learning. [paper] [preprint] [code]

Lopes-dos-Santos V, van de Ven GM, Morley A, Trouche S, Campo-Urizza N, Dupret D (2018), “Parsing hippocampal theta oscillations by nested spectral components during spatial exploration and memory-guided behavior”, Neuron, 100(4): 940-952. [paper] [commentary]

van de Ven GM, Trouche S, McNamara CG, Allen K, Dupret D (2016), “Hippocampal offline reactivation consolidates recently formed cell assembly patterns during sharp wave-ripples”, Neuron, 92(5): 968-974. [paper] [video abstract]

Trouche S, Perestenko PV, van de Ven GM, Bratley CT, McNamara CG, Campo-Urizza N, Black SL, Reijmers LG, Dupret D (2016), “Recoding a cocaine-place memory engram to a neutral engram in the hippocampus”, Nature Neuroscience, 19(4): 564-567. [paper]

Möttönen R, van de Ven GM, Watkins KE (2014), “Attention Fine-Tunes Auditory-Motor Processing of Speech Sounds”, The Journal of Neuroscience, 34(11): 4064-4069. [paper]

Pre-registered report

Hess T, Tuytelaars T van de Ven GM (2023), “Two complementary perspectives to continual learning: ask not only what to optimize, but also how”, Accepted pre-registered proposal at the 1st ContinualAI Unconference, full paper to appear in PMLR. [proposal]


van de Ven GM*, Soures N*, Kudithipudi D (2024), “Continual learning and catastrophic forgetting”, arXiv preprint, arXiv:2403.05175. [preprint]

Dziadzio S, Yıldız Ç, van de Ven GM, Trzciński T, Tuytelaars T, Bethge M (2023), “Disentangled continual learning: separating memory edits from model updates”, arXiv preprint, arXiv:2312.16731. [preprint]

Masip S, Rodriguez P, Tuytelaars T, van de Ven GM (2023), “Continual learning of diffusion models with generative distillation”, arXiv preprint, arXiv:2311.14028. [preprint] [code]

Hess T*, Verwimp E*, van de Ven GM, Tuytelaars T (2023), “Knowledge accumulation in continually learned representations and the issue of feature forgetting”, arXiv preprint, arXiv:2304.00933. [preprint]

Vogelstein JT*, Dey J*, Helm HS, LeVine W, Mehta RD, Tomita TM, Xu H, Geisa A, Wang Q, van de Ven GM, Gao C, Yang W, Tower B, Larson J, White CM, Priebe CE (2020), “Ensembling representations for synergistic lifelong learning with quasilinear complexity”, arXiv preprint, arXiv:2004.12908. [preprint] [code]

van de Ven GM, Tolias AS (2018), “Generative replay with feedback connections as a general strategy for continual learning”, arXiv preprint, arXiv:1809.10635. [preprint] [code]

* indicates joint first author