1 citations · 1 across the 6 of their papers we have counts for
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When is an Embedding Model More Promising than Another?
Maxime Darrin, Philippe Formont, Ismail Ben Ayed +2
Embedders play a central role in machine learning, projecting any object into numerical representations that can, in turn, be leveraged to perform various downstream tasks. The eva…
A Strong Baseline for Molecular Few-Shot Learning
Philippe Formont, Hugo Jeannin, Pablo Piantanida +1
Few-shot learning has recently attracted significant interest in drug discovery, with a recent, fast-growing literature mostly involving convoluted meta-learning strategies. We rev…
: Mutual Information for Task-Agnostic Summarization Evaluation
Maxime Darrin, Philippe Formont, Jackie Chi Kit Cheung +1
Assessing the quality of summarizers poses significant challenges. In response, we propose a novel task-oriented evaluation approach that assesses summarizers based on their capaci…