Publications associées à Causality: fundamental principles and tools

Publications associées (12)

Differences in Visual Perception in Humans and Deep Neural Networks

Visual grouping is the ability to combine parts of objects to form whole objects, and it is crucial for general and robust vision. Despite this, a comprehensive model of how the process works in humans remains elusive. In my thesis, I investigate how well ...

EPFL2025

High-resolution predictions of the coolant properties for the 3D PWR core with artificial neural networks based on CTF

Marianna Papadionysiou

PSI and North Carolina State University are developing a high-resolution multi-physics core solver for Pressurized Water Reactor (PWR) analysis in Cartesian geometry, using the neutron transport code nTRACER and two Machine Learning (ML) models providing t ...

2025

Drone-Based Urban Traffic Monitoring and User Behavior Prediction

Sohyeong Kim

As urban populations continue to expand and vehicle density increases, the need for effective traffic monitoring and management becomes increasingly critical. Urban areas face significant challenges such as growing congestion, safety concerns, and parking ...

EPFL2025

Do We Always Need the Simplicity Bias? Looking for Optimal Inductive Biases in the Wild

Liangze Jiang

Neural architectures tend to fit their data with relatively simple functions. This "simplicity bias" is widely regarded as key to their success. This paper explores the limits of this principle. Building on recent findings that the simplicity bias stems fr ...

IEEE2025

Quantification of Vaccine Waning as a Challenge Effect

Mats Julius Stensrud

Knowing whether vaccine protection wanes over time is important for health policy and drug development. However, quantifying waning effects is difficult. A simple contrast of vaccine efficacy at two different times compares different populations of individ ...

2024

Hierarchical Training of Deep Neural Networks Using Early Exiting

Pascal Frossard, Liza Carol Andrea Dunbar, Ahmet Caner Yüzügüler, Yamin Sepehri

Deep neural networks (DNNs) provide state-of-the-art accuracy for vision tasks, but they require significant resources for training. Thus, they are trained on cloud servers far from the edge devices that acquire the data. This issue increases communication ...

Ieee-Inst Electrical Electronics Engineers Inc2024

A Joint Graph Signal and Laplacian Denoising Network

Zepeng Zhang

Graph neural network (GNN) models have presented astonishing achievements in various application fields. However, they are shown to be vulnerable to adversarial attacks on graph structure and unnoticeable perturbations on the graph structure can cause sign ...

Institute of Electrical and Electronics Engineers Inc.2024

Neutron tomography and image registration methods to study local physical deformations and attenuation variations in treated archaeological iron nail samples

Ocson Reginald Cocen

This study presents a preliminary examination of the effects of environment changes post-excavation on heavily corroded archaeological Roman iron nails using neutron tomography and image registration techniques. Roman nails were exposed to either a high re ...

SPRINGER HEIDELBERG2024

Euclid preparation XLIII. Measuring detailed galaxy morphologies for Euclid with machine learning

Frédéric Courbin, Georges Meylan, Austin Chandler Peel

The Euclid mission is expected to image millions of galaxies at high resolution, providing an extensive dataset with which to study galaxy evolution. Because galaxy morphology is both a fundamental parameter and one that is hard to determine for large samp ...

2024

Online Learning and Information Exponents: On The Importance of Batch size, and Time/Complexity Tradeoffs

Florent Gérard Krzakala, Bruno Loureiro, Ludovic Théo Stephan, Yatin Dandi, Luca Pesce, Luca Arnaboldi

We study the impact of the batch

n_b

on the iteration time

T

of training two-layer neural networks with one-pass stochastic gradient descent (SGD) on multi-index target functions of isotropic covariates. We characterize the optimal batch size minimizin ...

JMLR2024

From STEM-EDXS data to phase separation and quantification using physics-guided NMF

Thomas Tim Holvoet, Cécile Hébert, Duncan Thomas Lindsay Alexander, Nathanaël Perraudin, Guillaume Romain Obozinski

We present the development of a new algorithm which combines state-of-the-art energy-dispersive x-ray (EDX) spectroscopy theory and a suitable machine learning formulation for the hyperspectral unmixing of scanning transmission electron microscope EDX spec ...

2024

AmadeusGPT: a natural language interface for interactive animal behavioral analysis

Alexander Mathis, Mackenzie Mathis, Shaokai Ye, Jessy Lauer, Mu Zhou

The process of quantifying and analyzing animal behavior involves translating the naturally occurring descriptive language of their actions into machine-readable code. Yet, codifying behavior analysis is often challenging without deep understanding of anim ...

2023