We have 3 of 60 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
Machine unlearning in large language models aims to remove unwanted knowledge while preserving the model's remaining capabilities. Although existing methods use retention objectives or restrict where edits occur, achieving the desired forgetting level can still leave collateral changes that impair non-target behavior....
A robust dynamic-expansion framework that integrates sample purification, selective recovery, and robust expert routing into a unified continual learning paradigm is proposed, and Bi-Prototype Purification (BPP) is introduced to identify suspicious samples by exploiting semantic discrepancies in feature space.
An innovative approach to robustness in continual learning systems is introduced, the Dynamic Siamese Expansion Framework (DSEF) that employs a Siamese backbone architecture, comprising static and dynamic components, to facilitate the learning of both global and local representations over time.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
