Publications associées à Sensitivity analysis in core diagnostics

Publications associées (18)

Learning Syntax Without Planting Trees: Understanding Hierarchical Generalization in Transformers

Transformers trained on natural language data have been shown to exhibit hierarchical generalization without explicitly encoding any structural bias. In this work, we investigate sources of inductive bias in transformer models and their training that could ...

MIT PRESS2025

Search for New Resonances Decaying to Pairs of Merged Diphotons in Proton-Proton Collisions at (Formula presented)

Jan Steggemann, Konstantin Androsov, Anna Mascellani, Joanna Malgorzata Wanczyk

A search is presented for an extended Higgs sector with two new particles, (Formula presented) and (Formula presented), in the process (Formula presented). Novel neural networks classify events with diphotons that are merged and determine the diphoton mass ...

American Physical Society (APS)2025

Tiny satellites, big challenges: A feasibility study of machine vision pose estimation for PocketQubes during conjunctions

Niki Sajjad, Mathieu Salzmann, Andrew Lawrence Price

This paper assesses the feasibility of estimating the position and attitude of a target satellite using computer vision, not during proximity operations, but during conjunctions at distances greater than 1 km. The selected target is a small 5 × 5 × 5 cm3 P ...

2025

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Mattia Atzeni

The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...

EPFL2024

Exploring High-Performance and Energy-Efficient Architectures for Edge AI-Enabled Applications

Joshua Alexander Harrison Klein

The desire and ability to place AI-enabled applications on the edge has grown significantly in recent years. However, the compute-, area-, and power-constrained nature of edge devices are stressed by the needs of the AI-enabled applications, due to a gener ...

EPFL2024

Error assessment of an adaptive finite elements-neural networks method for an elliptic parametric PDE

Maude Girardin, Marco Picasso, Alexandre Caboussat

We present a finite elements-neural network approach for the numerical approximation of parametric partial differential equations. The algorithm generates training data from finite element simulations, and uses a data -driven (supervised) feedforward neura ...

Elsevier Science Sa2024

Graph generative deep learning models with an application to circuit topologies

Igor Krawczuk

Modern integrated circuits are tiny yet incredibly complex technological artifacts, composed of millions and billions of individual structures working in unison. Managing their complexity and facilitating their design drove part of the co-evolution of mode ...

EPFL2024

Search for heavy neutral leptons in final states with electrons, muons, and hadronically decaying tau leptons in proton-proton collisions at √s=13 TeV

Jan Steggemann, Konstantin Androsov, Anna Mascellani, Joanna Malgorzata Wanczyk

A search for heavy neutral leptons (HNLs) of Majorana or Dirac type using proton-proton collision data at root s = 13TeV= 13 TeV is presented. The data were collected by the CMS experiment at the CERN LHC and correspond to an integrated luminosity of 138 f ...

Springer Nature2024

3DReact: Geometric Deep Learning for Chemical Reactions

Vignesh Ram Somnath, Andreas Krause, Anne-Clémence Corminboeuf, Rubén Laplaza Solanas, Puck Elisabeth van Gerwen, Ksenia Briling, Charlotte Cäcilie Gertrud Bunne

Geometric deep learning models, which incorporate the relevant molecular symmetries within the neural network architecture, have considerably improved the accuracy and data efficiency of predictions of molecular properties. Building on this success, we int ...

2024

Estimation of horizontal running power using foot-worn inertial measurement units

Kamiar Aminian, Mathieu Pascal Falbriard, Salil Apte

Feedback of power during running is a promising tool for training and determining pacing strategies. However, current power estimation methods show low validity and are not customized for running on different slopes. To address this issue, we developed thr ...

FRONTIERS MEDIA SA2023

Kernel Methods and Similarity Learning Applied in Computational Chemistry

Raimon Fabregat I De Aguilar-Amat

Over the last two decades, data-powered machine learning (ML) tools have profoundly transformed numerous scientific fields. In computational chemistry, machine learning applications have permitted faster predictions of chemical properties and provided powe ...

EPFL2022

An assessment of the structural resolution of various fingerprints commonly used in machine learning

Sandip De

Atomic environment fingerprints are widely used in computational materials science, from machine learning potentials to the quantification of similarities between atomic configurations. Many approaches to the construction of such fingerprints, also called ...

2021

Discourse Phenomena in Machine Translation

Lesly Sadiht Miculicich Werlen

Machine Translation (MT) has made considerable progress in the past two decades, particularly after the introduction of neural network models (NMT). During this time, the research community has mostly focused on modeling and evaluating MT systems at the se ...

EPFL2021

Lupulus: A Flexible Hardware Accelerator For Neural Networks

Andreas Peter Burg, Alexios Konstantinos Balatsoukas Stimming, Robert Giterman, Andreas Toftegaard Kristensen

Neural networks have become indispensable for a wide range of applications, but they suffer from high computational- and memory-requirements, requiring optimizations from the algorithmic description of the network to the hardware implementation. Moreover, ...

IEEE2020

Wavefront shaping and deep learning in fiber endoscopy

Eirini Kakkava

Fiber endoscopy plays an important role in the clinical diagnosis and treatment processes involved in modern medicine. Thin fiber probes can relay information from confined places in the human body that are inaccessible for conventional bulky microscopes. ...

EPFL2020

How Machine Learning is challenged by algorithmic complexity classes. From models to practices

Dominique Georges Léon Boullier

Machine Learning, or the learning methods used to constitute what is called Artificial Intelligence, is more diversified than is generally presented. Based in particular on the classification proposed by Domingos, the article begins by presenting different ...

SOC ANTHROPOLOGIE CONNAISSANCES2020

Search for B+ -> mu(+)nu(mu) and B+ -> mu N+ with inclusive tagging

Mingkui Wang, Olivier Schneider, Chao Wang, Sun Hee Kim

We report the result for a search for the leptonic decay of B+ -> mu(+)nu mu using the full Belle dataset of 711 fb(-1) of integrated luminosity at the gamma(4S) resonance. In the Standard Model leptonic B-meson decays are helicity and Cabibbo-Kobayashi-Ma ...

American Physical Society2020

Biomedical Image Reconstruction: From the Foundations to Deep Neural Networks

Michaël Unser, Michael Thompson McCann

This tutorial covers biomedical image reconstruction, from the foundational concepts of system modeling and direct reconstruction to modern sparsity and learning-based approaches. Imaging is a critical tool in biological research and medicine, and most ima ...

2019