1 citations · 1 across the 4 of their papers we have counts for
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Subspace-Boosted Model Merging
Ronald Skorobogat, Karsten Roth, Mariana-Iuliana Georgescu
Model merging enables the combination of multiple specialized expert models into a single model capable of performing multiple tasks. However, the benefits of merging an increasing…
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…
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…
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…