26 citations · 91 across the 22 of their papers we have counts for
3 papers · 1 filter
Minimax Active Learning
Sayna Ebrahimi, William Gan, Dian Chen +5
Active learning aims to develop label-efficient algorithms by querying the most representative samples to be labeled by a human annotator. Current active learning techniques either…
Remembering for the Right Reasons: Explanations Reduce Catastrophic Forgetting
Sayna Ebrahimi, Suzanne Petryk, Akash Gokul +4
The goal of continual learning (CL) is to learn a sequence of tasks without suffering from the phenomenon of catastrophic forgetting. Previous work has shown that leveraging memory…
Adversarial Continual Learning
Sayna Ebrahimi, Franziska Meier, Roberto Calandra +2
Continual learning aims to learn new tasks without forgetting previously learned ones. We hypothesize that representations learned to solve each task in a sequence have a shared st…