4 citations · 8 across the 7 of their papers we have counts for
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cs.CV2023★ 2 cited
CLIP-QDA: An Explainable Concept Bottleneck Model
Rémi Kazmierczak, Eloïse Berthier, Goran Frehse +1
In this paper, we introduce an explainable algorithm designed from a multi-modal foundation model, that performs fast and explainable image classification. Drawing inspiration from…
cs.LG2023
On Double Descent in Reinforcement Learning with LSTD and Random Features
David Brellmann, Eloïse Berthier, David Filliat +1
Temporal Difference (TD) algorithms are widely used in Deep Reinforcement Learning (RL). Their performance is heavily influenced by the size of the neural network. While in supervi…