Publications associées à Models of 802.11 Multi-Hop Networks: Theoretical Insights and Experimental Validation

Safe Deep Neural Networks

The capabilities of deep learning systems have advanced much faster than our ability to understand them. Whilst the gains from deep neural networks (DNNs) are significant, they are accompanied by a growing risk and gravity of a bad outcome. This is troubli ...

EPFL2024

Biophysically accurate and machine learning-based surrogate models to optimize neuroprosthesis design and operation

Simone Romeni

Electrical stimulation of the nervous system has emerged as a promising assistive technology in case of many injuries and illnesses across various parts of the nervous system. In particular, the invasive neuromodulation of the peripheral nervous system see ...

EPFL2024

Optimizing Dynamic Aperture Studies with Active Learning

Tatiana Pieloni, Massimo Giovannozzi, Ekaterina Krymova, Mike Seidel, Davide Di Croce

Dynamic aperture is an important concept for the study of non-linear beam dynamics in circular accelerators. It describes the extent of the phase-space region where a particle's motion remains bounded over a given number of turns. Understanding the feature ...

Iop Publishing Ltd2024

Benign Overfitting in Deep Neural Networks under Lazy Training

Volkan Cevher, Zhenyu Zhu, Grigorios Chrysos, Fanghui Liu

This paper focuses on over-parameterized deep neural networks (DNNs) with ReLU activation functions and proves that when the data distribution is well-separated, DNNs can achieve Bayesoptimal test error for classification while obtaining (nearly) zero-trai ...

2023

Towards Efficient Gas Leak Detection in Built Environments: Data-Driven Plume Modeling for Gas Sensing Robots

Alcherio Martinoli, Faezeh Rahbar, Chiara Ercolani, Wanting Jin

The deployment of robots for Gas Source Lo- calization (GSL) tasks in hazardous scenarios significantly reduces the risk to humans and animals. Gas sensing using mobile robots focuses primarily on simplified scenarios, due to the complexity of gas dispersi ...

2023

From Kernel Methods to Neural Networks: A Unifying Variational Formulation

Michaël Unser

The minimization of a data-fidelity term and an additive regularization functional gives rise to a powerful framework for supervised learning. In this paper, we present a unifying regularization functional that depends on an operator L\documentclass[12pt]{ ...

Springer2023

PINION: physics-informed neural network for accelerating radiative transfer simulations for cosmic reionization

Michele Bianco, Jean-Paul Richard Kneib, Emma Elizabeth Tolley

With the advent of the Square Kilometre Array Observatory (SKAO), scientists will be able to directly observe the Epoch of Reionization by mapping the distribution of neutral hydrogen at different redshifts. While physically motivated results can be simula ...

OXFORD UNIV PRESS2023

Reinforcement learning approach to control an inverted pendulum: A general framework for educational purposes

Jesus Sanchez Rodriguez

Machine learning is often cited as a new paradigm in control theory, but is also often viewed as empirical and less intuitive for students than classical model-based methods. This is particularly the case for reinforcement learning, an approach that does n ...

PUBLIC LIBRARY SCIENCE2023

Traversing Time Dependent Light Fields for Daylight Glare Evaluation

Stephen William Wasilewski

To understand how daylight gives shape and life to architectural spaces, whether existing or imagined, requires quantifying its dynamism and energy. Maintaining these details presents a challenge to simulation and analysis methods that flatten data into di ...

EPFL2023

REMOTE: Re-thinking Task Mapping on Wireless 2.5D Systems-on-Package for Hotspot Removal

David Atienza Alonso, Giovanni Ansaloni, Marina Zapater Sancho, Darong Huang, Rafael Medina Morillas

2.5D Systems-on-Package (SoPs) are composed by several chiplets placed on an interposer. They are becoming increasingly popular as they enable easy integration of electronic components in the same package and high fabrication yields. Nevertheless, they int ...

2023

Analysis of the influence of errors in DNA-based image coding

Touradj Ebrahimi, Davi Nachtigall Lazzarotto, Michela Testolina

In the last decade, DNA has been increasingly investigated as an alternative medium for cold data storage, presenting several advantages over standard hard drives such as a higher density, longer lifespan and lower energy consumption. However, such coding ...

2023

A Theory of Finite-Width Neural Networks: Generalization, Scaling Laws, and the Loss Landscape

Berfin Simsek

Deep learning has achieved remarkable success in various challenging tasks such as generating images from natural language or engaging in lengthy conversations with humans. The success in practice stems from the ability to successfully train massive neural ...

EPFL2023

Modeling and simulation of neocortical micro- and mesocircuitry. Part II: Physiology and experimentation

Henry Markram, Felix Schürmann, James Gonzalo King, Rajnish Ranjan, Srikanth Ramaswamy, Alexis Arnaudon, Eilif Benjamin Muller, Daniel Keller, Michael Reimann, Vishal Sood, Jean-Denis Georges Emile Courcol, Werner Alfons Hilda Van Geit, Giuseppe Chindemi, Michael Emiel Gevaert, Pramod Shivaji Kumbhar, Armando Romani, Christian Andreas Rössert, Judit Planas Carbonell, Genrich Ivaska, András Ecker, Daniela Egas Santander, Natali Barros Zulaica, Alexander Dietz, Omar Awile, Samuel Lieven D. Lapere, Matthias Wolf, Sirio Bolaños Puchet, Thomas Brice Delemontex, Jorge Blanco Alonso, Mustafa Anil Tuncel, Tanguy Pierre Louis Damart, Maria Reva, James Bryden Isbister, Darshan Mandge, Weina Ji, Aleksandra Zuzanna Teska, Polina Litvak, Gianluca Ficarelli, Joni Henrikki Herttuainen, ,

The DESI N-body Simulation Project - II. Suppressing sample variance with fast simulations

Yu Yu, Andrei Variu, Cheng Zhao, Hanyu Zhang

Dark Energy Spectroscopic Instrument (DESI) will construct a large and precise three-dimensional map of our Universe. The survey effective volume reaches similar to 20 h(-3) Gpc(3). It is a great challenge to prepare high-resolution simulations with a much ...

OXFORD UNIV PRESS2022

Realistic Modelling of Dynamics at Nanostructured Interfaces Relevant to Heterogeneous Catalysis

Kevin Rossi

The focus of this short review is directed towards investigations of the dynamics of nanostructured metallic heterogeneous catalysts and the evolution of interfaces during reaction-namely, the metal-gas, metal-liquid, and metal-support interfaces. Indeed, ...

MDPI2022

Sharp asymptotics on the compression of two-layer neural networks

Marco Mondelli

In this paper, we study the compression of a target two-layer neural network with N nodes into a compressed network with M < N nodes. More precisely, we consider the setting in which the weights of the target network are i.i.d. sub-Gaussian, and we minimiz ...

IEEE2022

High-order geometric integrators for nonadiabatic molecular quantum dynamics and their applications to explore conical intersections

Seonghoon Choi

Accurate simulations of molecular quantum dynamics are crucial for understanding numerous natural processes and experimental results. Yet, such high-accuracy simulations are challenging even for relatively simple systems where the Born-Oppenheimer approxim ...

EPFL2022

MaxwellNet: Physics-driven deep neural network training based on Maxwell's equations

Demetri Psaltis, Joowon Lim

Maxwell's equations govern light propagation and its interaction with matter. Therefore, the solution of Maxwell's equations using computational electromagnetic simulations plays a critical role in understanding light-matter interaction and designing optic ...

AIP Publishing2022

Efficient Simulation for Visual Comfort Evaluations

Marilyne Andersen, Jan Wienold, Stephen William Wasilewski

This paper provides a validation of a novel sampling, storage, and evaluation method named raytraverse that can quickly and accurately compute glare and visual comfort metrics including vertical illuminance, Daylight Glare Probability (DGP), and Unified Gl ...

2022

Adaptive analysis-aware defeaturing

Ondine Gabrielle Chanon

Removing geometrical details from a complex domain is a classical operation in computer aided design for simulation and manufacturing. This procedure simplifies the meshing process, and it enables faster simulations with less memory requirements. However, ...

EPFL2022