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

Publications associées (21)

Integrating AI across the Chemistry Discovery Cycle: Advancing Sustainable Chemistry through Digital Methods

The chemical industry faces mounting pressure to develop more sustainable processes while accelerating innovation to address climate challenges. Traditional discovery approaches in chemistry follow an iterative cycle: forming hypotheses about new molecules ...

EPFL2025

Structural equation models for multivariate extremes

Mario Krali

This thesis contributes to the modelling of multivariate rare events by combining extreme value theory and graphical modelling. The central theme involves structural equation models (SEMs), which are powerful tools used to express causal extremal dependenc ...

EPFL2025

Towards Unifying Evaluation of Counterfactual Explanations: Leveraging Large Language Models for Human-Centric Assessments

Rasmus Moorits Veski

As machine learning models evolve, maintaining transparency demands more human-centric explainable AI techniques. Counterfactual explanations, with roots in human reasoning, identify the minimal input changes needed to obtain a given output, and hence, are ...

Association for the Advancement of Artificial Intelligence2025

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

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

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

Floorplan Isovist Dataset

Mikhael Johanes

This dataset was created as part of the PhD project Machine Understanding of Architectural Space: From Analytical to Generative Applications at EPFL Media and Design Lab (LDM). It was derived from the Cubicasa5K dataset. The dataset contains preprocessed d ...

Zenodo2024

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

Image sensitive spectral response of semiconductor random network lasers

Kirsten Emilie Moselund

We experimentally study the spectral lasing response of on-chip InP network random lasers under illumination of different input image shapes. Deep-learning models have become increasingly omipresent throughout society. However, they are blighted by exponen ...

SPIE-INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING2024

High-dimensional non-convex landscapes and gradient descent dynamics

Davide Ghio

In these lecture notes we present different methods and concepts developed in statistical physics to analyze gradient descent dynamics in high-dimensional non-convex landscapes. Our aim is to show how approaches developed in physics, mainly statistical phy ...

2024

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

AMCG: a graph dual atomic-molecular conditional molecular generator

Andrea Cavalli

Drug design is both a time consuming and expensive endeavour. Computational strategies offer viable options to address this task; deep learning approaches in particular are indeed gaining traction for their capability of dealing with chemical structures. A ...

IOP Publishing2024

Self-supervised learning of wrist-worn daily living accelerometer data improves the automated detection of gait in older adults

Anisoara Ionescu

Progressive gait impairment is common among aging adults. Remote phenotyping of gait during daily living has the potential to quantify gait alterations and evaluate the effects of interventions that may prevent disability in the aging population. Here, we ...

Nature Research2024

High-frequency transients in buried insulated wires: Transmission line simulations and experimental validation

Farhad Rachidi-Haeri, Rafael Silva Alipio, Naiara Figueiredo Duarte

This paper presents experimental results on the transient response of a buried insulated wire subjected to fast transient signals. It reports measurements of voltage and current at the sending end, as well as the voltage at the receiving end of the insulat ...

Elsevier2024

ScoreNet: Learning Non-Uniform Attention and Augmentation for Transformer-Based Histopathological Image Classification

Jean-Philippe Thiran, Seyedbehzad Bozorgtabar, Thomas Grégoire Stegmüller, Antoine Jérôme Spahr

Progress in digital pathology is hindered by high-resolution images and the prohibitive cost of exhaustive localized annotations. The commonly used paradigm to categorize pathology images is patch-based processing, which often incorporates multiple instanc ...

IEEE COMPUTER SOC2023

New tools for phase segmentation and denoising of analytical STEM data for enhanced chemical sensitivity, applied to Earth mantle research

Hui Chen, Hui Chen

This thesis is dedicated to developing innovative methodologies that improve elemental quantification in scanning transmission electron microscopy (STEM) using energy-dispersive X-ray spectroscopy (EDXS). The primary motivation stems from a geochemistry pr ...

EPFL2023

A machine learning method for real-time numerical simulations of cardiac electromechanics

Alfio Quarteroni

We propose a machine learning-based method to build a system of differential equations that approximates the dynamics of 3D electromechanical models for the human heart, accounting for the dependence on a set of parameters. Specifically, our method permits ...

ELSEVIER SCIENCE SA2022

A Lagrangian decomposition scheme for choice-based optimization

Michel Bierlaire, Meritxell Pacheco Paneque

Choice-based optimization problems are the family of optimization problems that incorporate the stochasticity of individual preferences according to discrete choice models to make planning decisions. This integration brings non-convexity and nonlinearity t ...

PERGAMON-ELSEVIER SCIENCE LTD2022

Deep Neural Network to Accurately Predict Left Ventricular Systolic Function Under Mechanical Assistance

Simone Deparis, Luca Pegolotti

Characterizing left ventricle (LV) systolic function in the presence of an LV assist device (LVAD) is extremely challenging. We developed a framework comprising a deep neural network (DNN) and a 0D model of the cardiovascular system to predict parameters o ...

FRONTIERS MEDIA SA2021

Optimization of therapeutic antibodies by predicting antigen specificity from antibody sequence via deep learning

Bruno Emanuel Ferreira De Sousa Correia, Pablo Gainza Cirauqui

The optimization of therapeutic antibodies is time-intensive and resource-demanding, largely because of the low-throughput screening of full-length antibodies (approximately 1 x 10(3) variants) expressed in mammalian cells, which typically results in few o ...

NATURE RESEARCH2021