Publications associées à Capturing Dichotomic Solvent Behavior in Solute−Solvent Reactions with Neural Network Potentials

Publications associées (9)

Temporally-Consistent Surface Reconstruction Using Metrically-Consistent Atlases

Pascal Fua, Mathieu Salzmann, Shaifali Parashar, Jan Bednarík

We propose a method for unsupervised reconstruction of a temporally-consistent sequence of surfaces from a sequence of time-evolving point clouds. It yields dense and semantically meaningful correspondences between frames. We represent the reconstructed su ...

2025

Neural decoding and feature selection methods for closed-loop control of avoidance behavior

Mahsa Shoaran, Jinhan Liu

Objective. Many psychiatric disorders involve excessive avoidant or defensive behavior, such as avoidance in anxiety and trauma disorders or defensive rituals in obsessive-compulsive disorders. Developing algorithms to predict these behaviors from local fi ...

IOP Publishing2024

On the modelling of the fatigue-induced damage in Francis turbines start-up sequences

Elena Vagnoni, Martin Seydoux, Ekaterina Krymova, Alessandro Morabito

The European Green Deal is prescribing a very ambitious rise in the annual production of renewable energy sources, and grid regulation services are increasingly important in the course of a massive integration of intermittent renewable energy sources. Alth ...

IOP Publishing2024

Advances in Algorithms for Sampling, Optimization and Inference in Disordered Systems

Giovanni Piccioli

This thesis focuses on the development of advanced algorithmic techniques, primarily Markov Chain Monte Carlo (MCMC) methods, and message passing algorithms, to tackle high-dimensional optimization and inference problems. The algorithms used have a probabi ...

EPFL2024

Brain networks subserving functional core processes of emotions identified with componential modeling

Dimitri Nestor Alice Van De Ville

Despite a lack of scientific consensus on the definition of emotions, they are generally considered to involve several modifications in the mind, body, and behavior. Although psychology theories emphasized multi-componential characteristics of emotions, li ...

OXFORD UNIV PRESS INC2023

Learning sparse features can lead to overfitting in neural networks

Matthieu Wyart, Leonardo Petrini, Francesco Cagnetta

It is widely believed that the success of deep networks lies in their ability to learn a meaningful representation of the features of the data. Yet, understanding when and how this feature learning improves performance remains a challenge. For example, it ...

Iop Publishing Ltd2023

Persistent Homology with Improved Locality Information for more Effective Delineation

Pascal Fua, Kathryn Hess Bellwald, Mateusz Kozinski, Doruk Oner, Adélie Eliane Garin

Persistent Homology (PH) has been successfully used to train networks to detect curvilinear structures and to improve the topological quality of their results. However, existing methods are very global and ignore the location of topological features. In th ...

2022

ResLogit: A residual neural network logit model for data-driven choice modelling

Melvin Jian Wen Wong

This paper presents a novel deep learning-based travel behaviour choice model. Our proposed Residual Logit (ResLogit) model formulation seamlessly integrates a Deep Neural Network (DNN) architecture into a multinomial logit model. Recently, DNN models such ...

PERGAMON-ELSEVIER SCIENCE LTD2021

Decoupling Backpropagation using Constrained Optimization Methods

Martin Jaggi, Johanni Michael Brea

We propose BlockProp, a neural network training algorithm. Unlike backpropagation, it does not rely on direct top-to-bottom propagation of an error signal. Rather, by interpreting backpropagation as a constrained optimization problem we split the neural ne ...

2018