1 citations · 1 across the 6 of their papers we have counts for
3 papers · 1 filter
Learning Task-Agnostic Representations through Multi-Teacher Distillation
Philippe Formont, Maxime Darrin, Banafsheh Karimian +5
Casting complex inputs into tractable representations is a critical step across various fields. Diverse embedding models emerge from differences in architectures, loss functions, i…
Statistical Deficiency for Task Inclusion Estimation
Loïc Fosse, Frédéric Béchet, Benoît Favre +5
Tasks are central in machine learning, as they are the most natural objects to assess the capabilities of current models. The trend is to build general models able to address any t…
Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study
Eric Aubinais, Philippe Formont, Pablo Piantanida +1
Quantizing machine learning models has demonstrated its effectiveness in lowering memory and inference costs while maintaining performance levels comparable to those of the origina…