Publications associées à Loi d'extremum généralisée

A story of two transitions: From adhesive to abrasive wear and from ductile to brittle regime

Jean-François Molinari, Sacha Zenon Wattel

Atomistic simulations performed with a family of model potential with tunable hardness have proven to be a great tool for advancing the understanding of wear processes at the asperity level. They have been instrumental in finding a critical length scale, w ...

2024

Scalable and Privacy-Preserving Federated Principal Component Analysis

Jean-Pierre Hubaux, Juan Ramón Troncoso-Pastoriza, Jean-Philippe Léonard Bossuat, Apostolos Pyrgelis, David Jules Froelicher, Joao André Gomes de Sá e Sousa

Principal component analysis (PCA) is an essential algorithm for dimensionality reduction in many data science domains. We address the problem of performing a federated PCA on private data distributed among multiple data providers while ensuring data confi ...

IEEE COMPUTER SOC2023

Adversarial Parametric Pose Prior

Pascal Fua, Mathieu Salzmann, Anastasia Remizova, Andrey Davydov, Victor Constantin, Sina Honari

The Skinned Multi-Person Linear (SMPL) model represents human bodies by mapping pose and shape parameters to body meshes. However, not all pose and shape parameter values yield physically-plausible or even realistic body meshes. In other words, SMPL is und ...

IEEE2022

Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models

Volkan Cevher, Paul Thierry Yves Rolland

This paper demonstrates how to recover causal graphs from the score of the data distribution in non-linear additive (Gaussian) noise models. Using score matching algorithms as a building block, we show how to design a new generation of scalable causal disc ...

2022

Dalton: Learned Partitioning for Distributed Data Streams

Anastasia Ailamaki, Eleni Zapridou, Ioannis Mytilinis

To sustain the input rate of high-throughput streams, modern stream processing systems rely on parallel execution. However, skewed data yield imbalanced load assignments and create stragglers that hinder scalability. Deciding on a static partitioning for a ...

2022

Failure and success of the spectral bias prediction for Laplace Kernel Ridge Regression: the case of low-dimensional data

Matthieu Wyart, Umberto Maria Tomasini, Antonio Sclocchi

Recently, several theories including the replica method made predictions for the generalization error of Kernel Ridge Regression. In some regimes, they predict that the method has a 'spectral bias': decomposing the true function f* on the eigenbasis of the ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2022

Flood of 23 November 2019 on the French Riviera: a comparison of discharges estimated by image analysis and hydrological modelling

Gauthier Paul Daniel Marie Rousseau, Etienne Robert

The short and heavy rainfall events observed on the French Riviera often lead to flash floods on coastal catchments: during the flood of 2 October 2015, a peak discharge between 185 and 295 m(3)/s was estimated on the Brague at Biot at 8:00 PM, whereas the ...

ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD2022

H-1 Detected Relayed Dynamic Nuclear Polarization

David Lyndon Emsley, Moreno Lelli, Pierrick Berruyer, Andrea Bertarello

Recently, it has been shown that methods based on the dynamics of 1 H nuclear hyperpolarization in magic angle spinning (MAS) NMR experiments can be used to determine mesoscale structures in complex materials. However, these methods suffer from low sensiti ...

AMER CHEMICAL SOC2022

Feature distribution learning by passive exposure

David Pascucci, Gizay Ceylan

Humans can rapidly estimate the statistical properties of groups of stimuli, including their average and variability. But recent studies of so-called Feature Distribution Learning (FDL) have shown that observers can quickly learn even more complex aspects ...

ELSEVIER2022

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Loi d'extremum généralisée

A story of two transitions: From adhesive to abrasive wear and from ductile to brittle regime

Scalable and Privacy-Preserving Federated Principal Component Analysis

Using Gaming Footage as a Source of Internet Latency Information

Adversarial Parametric Pose Prior

Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models

Dalton: Learned Partitioning for Distributed Data Streams

Failure and success of the spectral bias prediction for Laplace Kernel Ridge Regression: the case of low-dimensional data

Flood of 23 November 2019 on the French Riviera: a comparison of discharges estimated by image analysis and hydrological modelling

H-1 Detected Relayed Dynamic Nuclear Polarization

Feature distribution learning by passive exposure

Graph Chatbot

Chattez avec Graph Search

A story of two transitions: From adhesive to abrasive wear and from ductile to brittle regime

Using Gaming Footage as a Source of Internet Latency Information

Flood of 23 November 2019 on the French Riviera: a comparison of discharges estimated by image analysis and hydrological modelling

Scalable and Privacy-Preserving Federated Principal Component Analysis

Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models

Feature distribution learning by passive exposure

Adversarial Parametric Pose Prior

H-1 Detected Relayed Dynamic Nuclear Polarization

Failure and success of the spectral bias prediction for Laplace Kernel Ridge Regression: the case of low-dimensional data

Dalton: Learned Partitioning for Distributed Data Streams