Publications associées à Structural equation models for multivariate extremes

Publications associées (11)

Sequential Representation Learning via Static-Dynamic Conditional Disentanglement

This paper explores self-supervised disentangled representation learning within sequential data, focusing on separating time-indep- endent and time-varying factors in videos. We propose a new model that breaks the usual independence assumption between thos ...

Springer Science and Business Media Deutschland GmbH2025

Integer traffic assignment problem: Algorithms and insights on random graphs

Rayan Harfouche, Lenka Zdeborová, Giovanni Piccioli

Path optimization is a fundamental concern across various real-world scenarios, ranging from traffic congestion issues to efficient data routing over the Internet. The traffic assignment problem (TAP) is a classic continuous optimization problem in this fi ...

2025

An Efficient Machine Learning Model for Lightning Localization via Lightning-Induced Voltages on Transmission Lines

Farhad Rachidi-Haeri, Marcos Rubinstein, Hamidreza Karami

In this paper, three machine learning (ML)-based approaches—XGBoost, Artificial Neural Network (ANN), and Random Forest algorithms—are compared for the localization of lightning through the analysis of lightninginduced voltages on power transmission lines. ...

URSI – International Union of Radio Science2024

Machine learning-aided generative molecular design

Philippe Schwaller, Jeff Guo

Machine learning has provided a means to accelerate early-stage drug discovery by combining molecule generation and filtering steps in a single architecture that leverages the experience and design preferences of medicinal chemists. However, designing mach ...

Nature Portfolio2024

Approximation in the extended functional tensor train format

Bonan Sun, Daniel Kressner, Christoph Max Strössner

This work proposes the extended functional tensor train (EFTT) format for compressing and working with multivariate functions on tensor product domains. Our compression algorithm combines tensorized Chebyshev interpolation with a low-rank approximation alg ...

Springer2024

Can you Teach Cell Biology using Computer Simulations?

Julian Charles Shillcock

Computational modelling is important in the life sciences, but students may not be greatly exposed to it in typical curricula.This paper describes an innovative, one-semester Master's level course in cell biology that uses computer simulations as the prima ...

European Society for Engineering Education (SEFI)2024

TEE4EHR: Transformer event encoder for better representation learning in electronic health records

David Atienza Alonso, Anisoara Ionescu, Hojjat Karami

Irregular sampling of time series in electronic health records (EHRs) is one of the main challenges for developing machine learning models. Additionally, the pattern of missing values in certain clinical variables is not at random but depends on the decisi ...

2024

Supplementary files for Machine learning for histological annotation and quantification of cortical layers

Olivier Burri, Julie Meystre

Creators Meystre Julie Olivier Burri Contributors Jean Jacquemier Description This dataset contains 7 QuPath projects. The raw data images linked to these projects and located in other Zenodo datasets need to be downloaded as well. The raw data contains im ...

Zenodo2024

Exact recovery and Bregman hard clustering of node-attributed Stochastic Block Model

Maximilien Claude Robert Dreveton

Network clustering tackles the problem of identifying sets of nodes (communities) that have similar connection patterns. However, in many scenarios, nodes also have attributes that are correlated with the clustering structure. Thus, network information (ed ...

2023

The Structure of Free Polyphony

Christoph Finkensiep

The human ability to perceive and understand music is remarkable. From an unstructured stream of acoustic input it creates a wide range of experiences, from psycho-acoustic effects to emotional and aesthetic responses. One such set of phenomena is the expe ...

EPFL2023

Are Gaussian data all you need? the extents and limits of universality in high-dimensional generalized linear estimation

Florent Gérard Krzakala, Ludovic Théo Stephan, Luca Pesce

In this manuscript we consider the problem of generalized linear estimation on Gaussian mixture data with labels given by a single-index model. Our first result is a sharp asymptotic expression for the test and training errors in the high-dimensional regim ...

PMLR2023