Philippe Formont
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View article: Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study
Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study Open
Quantizing machine learning models has demonstrated its effectiveness in lowering memory and inference costs while maintaining performance levels comparable to the original models. In this work, we investigate the impact of quantization pr…
View article: Statistical Deficiency for Task Inclusion Estimation
Statistical Deficiency for Task Inclusion Estimation Open
International audience
View article: When is an Embedding Model More Promising than Another?
When is an Embedding Model More Promising than Another? Open
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 evaluation of embedding models typically depends on domain-spe…
View article: A Strong Baseline for Molecular Few-Shot Learning
A Strong Baseline for Molecular Few-Shot Learning Open
Few-shot learning has recently attracted significant interest in drug discovery, with a recent, fast-growing literature mostly involving convoluted meta-learning strategies. We revisit the more straightforward fine-tuning approach for mole…
View article: $\texttt{COSMIC}$: Mutual Information for Task-Agnostic Summarization Evaluation
$\texttt{COSMIC}$: Mutual Information for Task-Agnostic Summarization Evaluation Open
Assessing the quality of summarizers poses significant challenges. In response, we propose a novel task-oriented evaluation approach that assesses summarizers based on their capacity to produce summaries that are useful for downstream task…