3 citations · 7 across the 13 of their papers we have counts for
4 papers · 2 filters
Disentangled Representation Learning with the Gromov-Monge Gap
Théo Uscidda, Luca Eyring, Karsten Roth +3
Learning disentangled representations from unlabelled data is a fundamental challenge in machine learning. Solving it may unlock other problems, such as generalization, interpretab…
Reflecting on the State of Rehearsal-free Continual Learning with Pretrained Models
Lukas Thede, Karsten Roth, Olivier J. Hénaff +2
With the advent and recent ubiquity of foundation models, continual learning (CL) has recently shifted from continual training from scratch to the continual adaptation of pretraine…
ETHER: Efficient Finetuning of Large-Scale Models with Hyperplane Reflections
Massimo Bini, Karsten Roth, Zeynep Akata +1
Parameter-efficient finetuning (PEFT) has become ubiquitous to adapt foundation models to downstream task requirements while retaining their generalization ability. However, the am…
Improving Intervention Efficacy via Concept Realignment in Concept Bottleneck Models
Nishad Singhi, Jae Myung Kim, Karsten Roth +1
Concept Bottleneck Models (CBMs) ground image classification on human-understandable concepts to allow for interpretable model decisions. Crucially, the CBM design inherently allow…