Tinne Tuytelaars

Co-observation in training data can dramatically enhance generalization in continual learning, revealing a new dimension beyond forgetting and plasticity.

Fine-tuned foundation models can significantly outperform human-inspired methods in compositional analysis, but at the expense of interpretability and generalization.

FF-JEPA transforms long-horizon planning by enabling goal-free trajectory optimization without the need for explicit goal images.

Forget catastrophic forgetting: modular memory, blending in-context and in-weight learning, offers a practical path to truly continual learning agents.