Concept
Weak topology
Related publications (88)
Shape Holomorphy of Boundary Integral Operators on Multiple Open Arcs
Fernando José Henriquez Barraza
We establish shape holomorphy results for general weakly- and hyper-singular boundary integral operators arising from second-order partial differential equations in unbounded two-dimensional domains with multiple finite-length open arcs. After recasting th ...
New York2024CONVERGENCE AND NONCONVERGENCE OF SCALED SELF-INTERACTING RANDOM WALKS TO BROWNIAN MOTION PERTURBED AT EXTREMA
We use generalized Ray-Knight theorems, introduced by B. Toth in 1996, together with techniques developed for excited random walks as main tools for establishing positive and negative results concerning convergence of some classes of diffusively scaled sel ...
Cleveland2023Multi-agent reinforcement learning with graph convolutional neural networks for optimal bidding strategies of generation units in electricity markets
Finding optimal bidding strategies for generation units in electricity markets would result in higher profit. However, it is a challenging problem due to the system uncertainty which is due to the lack of knowledge of the strategies of other generation uni ...
PERGAMON-ELSEVIER SCIENCE LTD2023Communication-efficient distributed training of machine learning models
In this thesis, we explore techniques for addressing the communication bottleneck in data-parallel distributed training of deep learning models. We investigate algorithms that either reduce the size of the messages that are exchanged between workers, or th ...
EPFL2023Decentralized learning made easy with DecentralizePy
Anne-Marie Kermarrec, Rafael Pereira Pires, Akash Balasaheb Dhasade, Rishi Sharma, Milos Vujasinovic
Decentralized learning (DL) has gained prominence for its potential benefits in terms of scalability, privacy, and fault tolerance. It consists of many nodes that coordinate without a central server and exchange millions of parameters in the inherently ite ...
2023Stochastic distributed learning with gradient quantization and double-variance reduction
Sebastian Urban Stich, Konstantin Mishchenko
We consider distributed optimization over several devices, each sending incremental model updates to a central server. This setting is considered, for instance, in federated learning. Various schemes have been designed to compress the model updates in orde ...
TAYLOR & FRANCIS LTD2022D-Cliques: Compensating for Data Heterogeneity with Topology in Decentralized Federated Learning
Anne-Marie Kermarrec, Erick Lavoie
The convergence speed of machine learning models trained with Federated Learning is significantly affected by non-independent and identically distributed (non-IID) data partitions, even more so in a fully decentralized setting without a central server. In ...
2022On The Convergence Of Stochastic Primal-Dual Hybrid Gradient
Volkan Cevher, Ahmet Alacaoglu
In this paper, we analyze the recently proposed stochastic primal-dual hybrid gradient (SPDHG) algorithm and provide new theoretical results. In particular, we prove almost sure convergence of the iterates to a solution with convexity and linear convergenc ...
SIAM PUBLICATIONS2022Early motor skill acquisition in healthy older adults: functional MRI and connectome correlates
decrement have been proposed, such as weakened acquisition of the motor skill. While the processes at play during the initial acquisition phase have been well-characterized in young adults, they were only scarcely investigated in older adults. The goal of ...
EPFL2022Intrinsic area near the origin for self-similar growth-fragmentations and related random surfaces
We study the behaviour of a natural measure defined on the leaves of the genealogical tree of some branching processes, namely self-similar growth-fragmentation processes. Each particle, or cell, is attributed a positive mass that evolves in continuous tim ...
2022