107 citations · 416 across the 27 of their papers we have counts for
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stat.ML2023
Choosing the parameter of the Fermat distance: navigating geometry and noise
Frédéric Chazal, Laure Ferraris, Pablo Groisman +3
The Fermat distance has been recently established as a useful tool for machine learning tasks when a natural distance is not directly available to the practitioner or to improve th…
stat.ML2023
MAGDiff: Covariate Data Set Shift Detection via Activation Graphs of Deep Neural Networks
Charles Arnal, Felix Hensel, Mathieu Carrière +4
Despite their successful application to a variety of tasks, neural networks remain limited, like other machine learning methods, by their sensitivity to shifts in the data: their p…