Publications related to Stochastic distributed learning with gradient quantization and double-variance reduction

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

Machine learning (ML) enables artificial intelligent (AI) agents to learn autonomously from data obtained from their environment to perform tasks. Modern ML systems have proven to be extremely effective, reaching or even exceeding human intelligence.Althou ...

EPFL2024

Efficient Continual Finite-Sum Minimization

Volkan Cevher, Efstratios Panteleimon Skoulakis, Leello Tadesse Dadi

Given a sequence of functions

f_1,\ldots,f_n

with

f_i:\mathcal{D}\mapsto \mathbb{R}

, finite-sum minimization seeks a point

{x}^\star \in \mathcal{D}

minimizing

\sum_{j=1}^nf_j(x)/n

. In this work, we propose a key twist into the finite-sum minimizat ...

2024

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

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

The upcoming Square Kilometre Array Observatory will produce images of neutral hydrogen distribution during the epoch of reionization by observing the corresponding 21-cm signal. However, the 21-cm signal will be subject to instrumental limitations such as ...

Oxford Univ Press2024

Perturbed Utility Stochastic Traffic Assignment

This paper develops a fast algorithm for computing the equilibrium assignment with the perturbed utility route choice (PURC) model. Without compromise, this allows the significant advantages of the PURC model to be used in large-scale applications. We form ...

Informs2024

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Anastasiia Koloskova

Distributed learning is the key for enabling training of modern large-scale machine learning models, through parallelising the learning process. Collaborative learning is essential for learning from privacy-sensitive data that is distributed across various ...

EPFL2024

WILD SOLUTIONS TO SCALAR EULER-LAGRANGE EQUATIONS

Carl Johan Peter Johansson

. We study very weak solutions to scalar Euler-Lagrange equations associated with quadratic convex functionals. We investigate whether W1,1 solutions are necessarily W 1,2 Nash and Schauder applicable. We answer this question positively for a suitable clas ...

Amer Mathematical Soc2024

Non-parametric IQC Multipliers in Data-Driven Robust Controller Synthesis

Alireza Karimi, Vaibhav Gupta, Elias Sebastian Klauser

The paper presents a robust data-driven controller synthesis method for generalised multi-input multioutput (MIMO) systems. Using the frequency response of a linear time-invariant (LTI) MIMO system and characterising perturbations through Integral Quadrati ...

2024

Functional-Basis Analysis of Non-Stationary Signals in Modern Power Grids: Theory and Implementation in Embedded Systems

Alexandra Cameron Karpilow

Situational awareness strategies are essential for the reliable and secure operation of the electric power grid which represents critical infrastructure in modern society. With the rise of converter-interfaced renewable generation and the consequent shift ...

EPFL2024

On the number of regions of piecewise linear neural networks

Michaël Unser, Alexis Marie Frederic Goujon

Many feedforward neural networks (NNs) generate continuous and piecewise-linear (CPWL) mappings. Specifically, they partition the input domain into regions on which the mapping is affine. The number of these so-called linear regions offers a natural metric ...

2024

Federated learning with uncertainty-based client clustering for fleet-wide fault diagnosis

Olga Fink, Hao Lu, Chao Hu

Operators from various industries have been pushing the adoption of wireless sensing nodes for industrial monitoring, and such efforts have produced sizeable condition monitoring datasets that can be used to build diagnosis algorithms capable of warning ma ...

2024

Stochastic distributed learning with gradient quantization and double-variance reduction

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Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

Efficient Continual Finite-Sum Minimization

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Perturbed Utility Stochastic Traffic Assignment

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

WILD SOLUTIONS TO SCALAR EULER-LAGRANGE EQUATIONS

Non-parametric IQC Multipliers in Data-Driven Robust Controller Synthesis

Functional-Basis Analysis of Non-Stationary Signals in Modern Power Grids: Theory and Implementation in Embedded Systems

On the number of regions of piecewise linear neural networks

Federated learning with uncertainty-based client clustering for fleet-wide fault diagnosis

Perturbed Utility Stochastic Traffic Assignment

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Efficient Continual Finite-Sum Minimization

WILD SOLUTIONS TO SCALAR EULER-LAGRANGE EQUATIONS

Functional-Basis Analysis of Non-Stationary Signals in Modern Power Grids: Theory and Implementation in Embedded Systems

Non-parametric IQC Multipliers in Data-Driven Robust Controller Synthesis

On the number of regions of piecewise linear neural networks

Federated learning with uncertainty-based client clustering for fleet-wide fault diagnosis