arXiv AI
From Order to Distribution: An Exact Operator Framework for Forgetting in Continual Learning
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv Machine Learning
1d ago
PMF-CL: Pareto-Minimal-Forgetting Continual Learner for Conflicting Tasks
arXiv:2605. 19145v3 Announce Type: replace Abstract: In the literature, many continual learning (CL) algorithms have been proposed to address the issue of catastrophic forgetting in ML models (i.
arXiv Machine Learning
Aug 11
Catastrophic Forgetting in Continual Reinforcement Learning
arXiv:2608. 08673v1 Announce Type: new Abstract: This work explores the relationship between task similarity and catastrophic forgetting in reinforcement learning.
arXiv Machine Learning
Jul 28
Forgetting is Everywhere
arXiv:2511. 04666v4 Announce Type: replace Abstract: A fundamental challenge in developing general learning algorithms is their tendency to forget past knowledge as they adapt to new data.
arXiv Machine Learning
Jun 29
Retaining by Doing: The Role of On-Policy Data in Mitigating Forgetting
arXiv:2510. 18874v3 Announce Type: replace Abstract: Adapting language models (LMs) to new tasks via post-training carries the risk of degrading existing capabilities -- a phenomenon classically known as catastrophic forgetting.
arXiv AI
Jul 13
Interference and Retention in Continual Learning
arXiv:2607. 09202v1 Announce Type: cross Abstract: Continual learning commonly relies on post-hoc mechanisms such as replay, elastic regularization, or distillation.
arXiv Machine Learning
Sep 15
Realistic Continual Learning Approach using Pre-trained Models
arXiv:2404.07729v2 Announce Type: replace Abstract: Continual learning (CL) evaluates adaptability in learning solutions to retain knowledge. Our research addresses the challenge of catastrophic forg...