Publications associées à Geometry of the Loss Landscape in Overparameterized Neural Networks: Symmetries and Invariances

Altruism, reciprocity, and tokens to reward forwarding data: Is that fair?

Verónica del Carmen Estrada Galiñanes, Arman Babaei

Decentralized storage networks offer services with intriguing possibilities to reduce inequalities in an extremely centralized market. Fair distribution of rewards, however, is still a persistent problem in the current generation of decentralized applicati ...

2024

Towards an Understanding of Hydraulic Sensitivity: Graph Theory Contributions to Water Distribution Analysis

Jérôme Chenal

Water distribution systems (WDSs) are complex networks with numerous interconnected junctions and pipes. The robustness and reliability of these systems are critically dependent on their network structure, necessitating detailed analysis for proactive leak ...

MDPI2024

Fast and Future: Towards Efficient Forecasting in Video Semantic Segmentation

Evann Pierre Guy Courdier

Deep learning has revolutionized the field of computer vision, a success largely attributable to the growing size of models, datasets, and computational power. Simultaneously, a critical pain point arises as several computer vision applications are deploye ...

EPFL2024

Global, Passive Detection of Connection Tampering

Louis-Henri Manuel Jakob Merino

In-network devices around the world monitor and tamper with connections for many reasons, including intrusion prevention, combating spam or phishing, and country-level censorship. Connection tampering seeks to block access to specific domain names or keywo ...

Assoc Computing Machinery2023

Equilibria in Network Constrained Energy Markets

Leonardo Massai

We study an energy market composed of producers who compete to supply energy to different markets and want to maximize their profits. The energy market is modeled by a graph representing a constrained power network where nodes represent the markets and lin ...

Elsevier2023

Estimating the Topology of Preferential Attachment Graphs Under Partial Observability

Ali H. Sayed, Michele Cirillo

This work addresses the problem of learning the topology of a network from the signals emitted by the network nodes. These signals are generated over time through a linear diffusion process, where neighboring nodes exchange messages according to the underl ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Short-term dynamics of drainage density based on a combination of channel flow state surveys and water level measurements

Andrea Rinaldo, Izabela Bujak-Ozga, Jana Freiin von Freyberg

Headwater streams often experience intermittent flow. Consequently, the flowing drainage network expands and contracts and the flowing drainage density (DD) varies over time. Monitoring the DD dynamics is essential to understand the processes controlling i ...

Wiley2023

Artificial Neural Network Training on an Optical Processor via Direct Feedback Alignment

Florent Gérard Krzakala, Julien Marcel Daniel Emmanuel Launay

Artificial Neural Networks (ANN) are habitually trained via the back-propagation (BP) algorithm. This approach has been extremely successful: Current models like GPT-3 have O(10 11 ) parameters, are trained on O(10 11 ) words and produce awe-inspiring resu ...

IEEE2023

Estimating pollution spread in water networks as a Schrödinger bridge problem with partial information

Isabel Haasler

Incidents where water networks are contaminated with microorganisms or pollutants can result in a large number of infected or ill persons, and it is therefore important to quickly detect, localize and estimate the spread and source of the contamination. In ...

Elsevier2023

Caching and Neutrality

Aikaterini Argyraki, Pavlos Nikolopoulos, Muhammad Abdullah

We are used to defining network neutrality as absence of traffic differentiation, like policing or shaping. These mechanisms, however, are often not what determines end-users’ quality of experience (QoE). Most content today is accessed through edge caches, ...

ACM Association for Computing Machinery2023

Non-volatile data storage medium and system

Martin Vetterli, Yves Bellouard, Ruben Ricca

A data storage medium (2) comprising a stacked plurality of layers (9), each layer composed of a layer material selected from a group comprising at least two different dielectric materials, adjacent layers being formed of different materials, and at least ...

2023

Model Predictive Control for Multi-Agent Systems under Limited Communication and Time-Varying Network Topology

Melanie Nicole Zeilinger, Danilo Saccani

In control system networks, reconfiguration of the controller when agents are leaving or joining the network is still an open challenge, in particular when operation constraints that depend on each agent's behavior must be met. Drawing our motivation from ...

IEEE2023

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

Fundamental Limits in Statistical Learning Problems: Block Models and Neural Networks

Elisabetta Cornacchia

This thesis focuses on two selected learning problems: 1) statistical inference on graphs models, and, 2) gradient descent on neural networks, with the common objective of defining and analysing the measures that characterize the fundamental limits. In th ...

EPFL2023

Community-based reconstruction and simulation of a full-scale model of region CA1 of rat hippocampus

Henry Markram, Felix Schürmann, James Gonzalo King, Srikanth Ramaswamy, Juan Bautista Hernando Vieites, Alexis Arnaudon, Eilif Benjamin Muller, Michael Reimann, Huanxiang Lu, Ying Shi, Marwan Muhammad Ahmed Abdellah, Vishal Sood, Jean-Denis Georges Emile Courcol, Werner Alfons Hilda Van Geit, Lida Kanari, Juan Luis Riquelme Roman, Michael Emiel Gevaert, Pramod Shivaji Kumbhar, Liesbeth Maria L Vanherpe, Armando Romani, Cyrille Pierre Henri Favreau, Michele Migliore, Mohameth François Sy, Szabolcs Kali, Judit Planas Carbonell, Genrich Ivaska, Sàra Sàray, Carmen Alina Lupascu, Rosanna Migliore, Audrey Mercer, András Ecker, Alberto Antonietti, Cristina Colangelo, Anna-Kristin Kaufmann, Julian Martin Budd, Elisabetta Giacalone, Thomas Brice Delemontex, Davide Bella, Fabien Petitjean, , , , ,

Localizing Traffic Differentiation

Aikaterini Argyraki, Pavlos Nikolopoulos, Zeinab Shmeis, Muhammad Abdullah

Network neutrality is important for users, content providers, policymakers, and regulators interested in understanding how network providers differentiate performance. When determining whether a network differentiates against certain traffic, it is importa ...

2023

A hybrid Selective Laser Sintering based process for the additive manufacturing of architectured composites

Erwan Philippe Guenier

Additive Manufacturing also commonly referred as 3D printing has been given a lot of interest lately. Market is growing exponentially and the aim for obtaining more complex and ready-to use parts is emerging. All the 3D printing processes consist in buildi ...

EPFL2023

Predicting nonlinear optical scattering with physics-driven neural networks

Demetri Psaltis, Ahmed Ayoub, Carlo Gigli

Deep neural networks trained on physical losses are emerging as promising surrogates for nonlinear numerical solvers. These tools can predict solutions to Maxwell's equations and compute gradients of output fields with respect to the material and geometric ...

AIP Publishing2023

Arrest Mechanisms of Buoyant Hydraulic Fractures

Andreas Möri, Brice Tanguy Alphonse Lecampion, Dmitriy Garagash, Carlo Peruzzo

Hydraulic fracturing (HF) treatments can form widespread fractures. Understanding their containment at depth is critical, given the positive buoyancy contrast between the fracturing fluid and the surrounding rock, promoting upward growth. We study arrest m ...

American Rock Mechanics Association (ARMA), OnePetro2023

Worst-Case Delay Analysis of Time-Sensitive Networks with Network Calculus

Seyed Mohammadhossein Tabatabaee

Time-sensitive networks, as in the context of IEEE Time-Sensitive Networking (TSN) and IETF Deterministic Networking (DetNet), offer deterministic services with guaranteed bounded latency in order to support safety-critical applications. In this thesis, we ...

EPFL2023