Three scenarios for continual learning. - Gido M. van de Ven; Andreas S. Tolias (CoRR, 2019)

Energy-Based Models for Continual Learning. - Shuang Li 013; Yilun Du; Gido M. van de Ven; Antonio Torralba 001; Igor Mordatch (CoRR, 2020)

Avalanche: An End-to-End Library for Continual Learning. - Vincenzo Lomonaco; Lorenzo Pellegrini; Andrea Cossu; Antonio Carta; Gabriele Graffieti; Tyler L. Hayes; Matthias De Lange; Marc Masana; Jary Pomponi; Gido M. van de Ven; Martin Mundt; Qi She; et al. (CVPR Workshops, 2021)

Class-Incremental Learning With Generative Classifiers. - Gido M. van de Ven; Zhe Li 002; Andreas S. Tolias (CVPR Workshops, 2021)

Natural continual learning: success is a journey, not (just) a destination. - Ta-Chu Kao; Kristopher T. Jensen; Gido van de Ven; Alberto Bernacchia; Guillaume Hennequin (NeurIPS, 2021)

Avalanche: an End-to-End Library for Continual Learning. - Vincenzo Lomonaco; Lorenzo Pellegrini; Andrea Cossu; Antonio Carta; Gabriele Graffieti; Tyler L. Hayes; Matthias De Lange; Marc Masana; Jary Pomponi; Gido M. van de Ven; Martin Mundt; Qi She; et al. (CoRR, 2021)

Energy-Based Models for Continual Learning. - Shuang Li 013; Yilun Du; Gido van de Ven; Igor Mordatch (CoLLAs, 2022)

Biological underpinnings for lifelong learning machines. - Dhireesha Kudithipudi; Mario Aguilar-Simon; Jonathan Babb; Maxim Bazhenov; Douglas Blackiston; Josh C. Bongard; Andrew P. Brna; Suraj Chakravarthi Raja; Nick Cheney; Jeff Clune; Anurag Reddy Daram; Stefano Fusi; et al. (Nat. Mach. Intell., 2022)

Three types of incremental learning. - Gido M. van de Ven; Tinne Tuytelaars; Andreas S. Tolias (Nat. Mac. Intell., 2022)

Continual evaluation for lifelong learning: Identifying the stability gap. - Matthias De Lange; Gido M. van de Ven; Tinne Tuytelaars (ICLR, 2023)

Two Complementary Perspectives to Continual Learning: Ask Not Only What to Optimize, But Also How. - Timm Hess; Tinne Tuytelaars; Gido M. van de Ven (CoRR, 2023)

Disentangled Continual Learning: Separating Memory Edits from Model Updates. - Sebastian Dziadzio; Çagatay Yildiz; Gido M. van de Ven; Tomasz Trzcinski; Tinne Tuytelaars; Matthias Bethge (CoRR, 2023)

Deep Continual Learning (Dagstuhl Seminar 23122). - Tinne Tuytelaars; Bing Liu; Vincenzo Lomonaco; Gido van de Ven; Andrea Cossu (Dagstuhl Reports, 2023)

Infinite dSprites for Disentangled Continual Learning: Separating Memory Edits from Generalization. - Sebastian Dziadzio; Çagatay Yildiz; Gido M. van de Ven; Tomasz Trzcinski; Tinne Tuytelaars; Matthias Bethge (CoLLAs, 2024)

Continual Learning of Diffusion Models with Generative Distillation. - Sergi Masip; Pau Rodríguez; Tinne Tuytelaars; Gido M. van de Ven (CoLLAs, 2024)

Prediction Error-based Classification for Class-Incremental Learning. - Michal Zajac 005; Tinne Tuytelaars; Gido M. van de Ven (ICLR, 2024)

Continual Learning and Catastrophic Forgetting. - Gido M. van de Ven; Nicholas Soures; Dhireesha Kudithipudi (CoRR, 2024)

Aligning Generalisation Between Humans and Machines. - Filip Ilievski; Barbara Hammer; Frank van Harmelen; Benjamin Paassen; Sascha Saralajew; Ute Schmid; Michael Biehl; Marianna Bolognesi; Xin Luna Dong; Kiril Gashteovski; Pascal Hitzler; Giuseppe Marra; et al. (CoRR, 2024)

Knowledge Accumulation in Continually Learned Representations and the Issue of Feature Forgetting. - Timm Hess; Eli Verwimp; Gido M. van de Ven; Tinne Tuytelaars (Trans. Mach. Learn. Res., 2024)

Continual Learning: Applications and the Road Forward. - Eli Verwimp; Rahaf Aljundi; Shai Ben-David; Matthias Bethge; Andrea Cossu; Alexander Gepperth; Tyler L. Hayes; Eyke Hüllermeier; Christopher Kanan; Dhireesha Kudithipudi; Christoph H. Lampert; Martin Mundt; et al. (Trans. Mach. Learn. Res., 2024)

Continual learning in the presence of repetition. - Hamed Hemati; Lorenzo Pellegrini; Xiaotian Duan; Zixuan Zhao; Fangfang Xia; Marc Masana; Benedikt Tscheschner; Eduardo E. Veas; Yuxiang Zheng; Shiji Zhao; Shao-Yuan Li; Sheng-Jun Huang; et al. (Neural Networks, 2025)

On the Computation of the Fisher Information in Continual Learning. - Gido M. van de Ven (CoRR, 2025)

Continual Learning Should Move Beyond Incremental Classification. - Rupert Mitchell; Antonio Alliegro; Raffaello Camoriano; Dustin Carrión-Ojeda; Antonio Carta; Georgia Chalvatzaki; Nikhil Churamani; Carlo D'Eramo; Samin Hamidi; Robin Hesse; Fabian Hinder; Roshni Kamath; et al. (CoRR, 2025)

Forgetting of task-specific knowledge in model merging-based continual learning. - Timm Hess; Gido M. van de Ven; Tinne Tuytelaars (CoRR, 2025)