Gido M. van de Ven

According to our database, Gido M. van de Ven authored at least 29 papers between 2019 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2026

Cross-Modal Prototype Alignment and Mixing for Training-Free Few-Shot Classification.

Dipam Goswami, Simone Magistri, Gido M. van de Ven, Bartlomiej Twardowski, Andrew D. Bagdanov, Tinne Tuytelaars, Joost van de Weijer

CoRR, March, 2026

Modular Memory is the Key to Continual Learning Agents.

Vaggelis Dorovatas, Malte Schwerin, Andrew D. Bagdanov, Lucas Caccia, Antonio Carta, Laurent Charlin, Barbara Hammer, Tyler L. Hayes, Timm Hess, Christopher Kanan, Dhireesha Kudithipudi, Xialei Liu, Vincenzo Lomonaco, Jorge Mendez-Mendez, Darshan Patil, Ameya Prabhu, Elisa Ricci, Tinne Tuytelaars, Gido M. van de Ven, Liyuan Wang, Joost van de Weijer, Jonghyun Choi, Martin Mundt, Rahaf Aljundi

CoRR, March, 2026

Revisiting Weight Regularization for Low-Rank Continual Learning.

Yaoyue Zheng, Yin Zhang, Joost van de Weijer, Gido M. van de Ven, Shaoyi Du, Xuetao Zhang, Zhiqiang Tian

CoRR, February, 2026

Putting a Face to Forgetting: Continual Learning meets Mechanistic Interpretability.

Sergi Masip, Gido M. van de Ven, Javier Ferrando, Tinne Tuytelaars

CoRR, January, 2026

2025

Simple Lifelong Learning Machines.

Joshua T. Vogelstein, Jayanta Dey, Hayden S. Helm, Will LeVine, Ronak D. Mehta, Tyler M. Tomita, Haoyin Xu, Ali Geisa, Qingyang Wang, Gido M. van de Ven, Chenyu Gao, Weiwei Yang, Bryan Tower, Jonathan Larson, Christopher M. White, Carey E. Priebe

IEEE Trans. Pattern Anal. Mach. Intell., November, 2025

Forgetting of task-specific knowledge in model merging-based continual learning.

Timm Hess, Gido M. van de Ven, Tinne Tuytelaars

CoRR, July, 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, Vincenzo Lomonaco, Subarnaduti Paul, Francesca Pistilli, Tinne Tuytelaars, Gido M. van de Ven, Kristian Kersting, Simone Schaub-Meyer, Martin Mundt

CoRR, February, 2025

On the Computation of the Fisher Information in Continual Learning.

Gido M. van de Ven

CoRR, February, 2025

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, Vincenzo Lomonaco, Gido M. van de Ven

Neural Networks, 2025

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, Razvan Pascanu, Adrian Popescu, Andreas S. Tolias, Joost van de Weijer, Bing Liu, Vincenzo Lomonaco, Tinne Tuytelaars, Gido M. van de Ven

Trans. Mach. Learn. Res., 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

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, Pasquale Minervini, Martin Mundt, Axel-Cyrille Ngonga Ngomo, Alessandro Oltramari, Gabriella Pasi, Zeynep G. Saribatur, Luciano Serafini, John Shawe-Taylor, Vered Shwartz, Gabriella Skitalinskaya, Clemens Stachl, Gido M. van de Ven, Thomas Villmann

CoRR, 2024

Continual Learning and Catastrophic Forgetting.

Gido M. van de Ven, Nicholas Soures, Dhireesha Kudithipudi

CoRR, 2024

Prediction Error-based Classification for Class-Incremental Learning.

Michal Zajac, Tinne Tuytelaars, Gido M. van de Ven

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Continual Learning of Diffusion Models with Generative Distillation.

Sergi Masip, Pau Rodríguez, Tinne Tuytelaars, Gido M. van de Ven

Proceedings of the Conference on Lifelong Learning Agents, 2024

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

Proceedings of the Conference on Lifelong Learning Agents, 2024

2023

Deep Continual Learning (Dagstuhl Seminar 23122).

Tinne Tuytelaars, Bing Liu, Vincenzo Lomonaco, Gido van de Ven, Andrea Cossu

Dagstuhl Reports, March, 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

Continual evaluation for lifelong learning: Identifying the stability gap.

Matthias De Lange, Gido M. van de Ven, Tinne Tuytelaars

Proceedings of the Eleventh International Conference on Learning Representations, 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

Proceedings of the 1st ContinualAI Unconference, 9 October 2023, Virtual, 2023

2022

Three types of incremental learning.

Gido M. van de Ven, Tinne Tuytelaars, Andreas S. Tolias

Nat. Mac. Intell., December, 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, Peter Helfer, Leslie Kay, Nicholas Ketz, Zsolt Kira, Soheil Kolouri, Jeffrey L. Krichmar, Sam Kriegman, Michael Levin, Sandeep Madireddy, Santosh Manicka, Ali Marjaninejad, Bruce McNaughton, Risto Miikkulainen, Zaneta Navratilova, Tej Pandit, Alice Parker, Praveen K. Pilly, Sebastian Risi, Terrence J. Sejnowski, Andrea Soltoggio, Nicholas Soures, Andreas S. Tolias, Darío Urbina-Meléndez, Francisco J. Valero Cuevas, Gido M. van de Ven, Joshua T. Vogelstein, Felix Wang, Ron Weiss, Angel Yanguas-Gil, Xinyun Zou, Hava T. Siegelmann

Nat. Mach. Intell., 2022

Energy-Based Models for Continual Learning.

Shuang Li, Yilun Du, Gido van de Ven, Igor Mordatch

Proceedings of the Conference on Lifelong Learning Agents, 2022

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, Keiland W. Cooper, Jeremy Forest, Eden Belouadah, Simone Calderara, German Ignacio Parisi, Fabio Cuzzolin, Andreas S. Tolias, Simone Scardapane, Luca Antiga, Subutai Amhad, Adrian Popescu, Christopher Kanan, Joost van de Weijer, Tinne Tuytelaars, Davide Bacciu, Davide Maltoni

CoRR, 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

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Class-Incremental Learning With Generative Classifiers.

Gido M. van de Ven, Zhe Li, Andreas S. Tolias

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 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, Keiland W. Cooper, Jeremy Forest, Eden Belouadah, Simone Calderara, German Ignacio Parisi, Fabio Cuzzolin, Andreas S. Tolias, Simone Scardapane, Luca Antiga, Subutai Ahmad, Adrian Popescu, Christopher Kanan, Joost van de Weijer, Tinne Tuytelaars, Davide Bacciu, Davide Maltoni

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2021

2020

Energy-Based Models for Continual Learning.

Shuang Li, Yilun Du, Gido M. van de Ven, Antonio Torralba, Igor Mordatch

CoRR, 2020

2019

Three scenarios for continual learning.

Gido M. van de Ven, Andreas S. Tolias

CoRR, 2019