180 citations
- Université Paris-SaclayFR185 papers
- Centre National de la Recherche ScientifiqueFR119 papers
- Institut national de recherche en sciences et technologies du numériqueFR29 papers
- Laboratoire Interdisciplinaire de PhysiqueFR26 papers
- Sorbonne UniversitéFR20 papers
- Centre Inria de SaclayFR17 papers
- CentraleSupélecFR15 papers
- Local Initiatives Support CorporationUS15 papers
- Université Paris CitéFR15 papers
- Universidad Complutense de MadridES14 papers
- École Normale Supérieure - PSLFR11 papers
- Gleason (United States)US10 papers
39 papers · 1 filter
PRIVET: PRoximIty leakage detection Via Extreme value Theory
Antoine Szatkownik, Aurélien Decelle, Beatriz Seoane +7
Deep generative models are often trained on sensitive data, such as genetic sequences, health data, or more broadly, any copyrighted, licensed or protected content. This raises cri…
AMStraMGRAM: Adaptive Multi-cutoff Strategy Modification for ANaGRAM
Nilo Schwencke, Cyriaque Rousselot, Alena Shilova +1
Recent works have shown that natural gradient methods can significantly outperform standard optimizers when training physics-informed neural networks (PINNs). In this paper, we ana…
The Environmental Impacts of Language Model Training Keep Rising Now is the Time to Catch Impacts on the Rebound
Clément Morand, Anne-Laure Ligozat, Aurélie Névéol
Recent Machine Learning (ML) approaches have shown increased performance on benchmarks at the cost of escalating compute demands. Hardware, algorithmic and carbon optimizations hav…
Position: Causal Machine Learning Requires Rigorous Synthetic Experiments for Broader Adoption
Audrey Poinsot, Panayiotis Panayiotou, Alessandro Leite +3
Causal machine learning has the potential to revolutionize decision-making by combining the predictive power of machine learning algorithms with the theory of causal inference. How…
StaQ: a Finite Memory Approach to Discrete Action Policy Mirror Descent
Alex Davey, Alena Shilova, Brahim Driss +1
In Reinforcement Learning (RL), regularization with a Kullback-Leibler divergence that penalizes large deviations between successive policies has emerged as a popular tool both in…
Integrating Fourier Neural Operators with Diffusion Models to improve Spectral Representation of Synthetic Earthquake Ground Motion Response
Niccolò Perrone, Fanny Lehmann, Hugo Gabrielidis +2
Nuclear reactor buildings must be designed to withstand the dynamic load induced by strong ground motion earthquakes. For this reason, their structural behavior must be assessed in…