Publications associées à When crowding of crowding leads to uncrowding

Robust Distributed Learning: Tight Error Bounds and Breakdown Point under Data Heterogeneity

Rachid Guerraoui, Nirupam Gupta, Rafaël Benjamin Pinot, Youssef Allouah, Geovani Rizk

The theory underlying robust distributed learning algorithms, designed to resist adversarial machines, matches empirical observations when data is homogeneous. Under data heterogeneity however, which is the norm in practical scenarios, established lower bo ...

2023

Acute TNF alpha levels predict cognitive impairment 6-9 months after COVID-19 infection

Dimitri Nestor Alice Van De Ville, Alessandra Griffa, Alexandre Cionca, Idris Guessous

Background: A neurocognitive phenotype of post-COVID-19 infection has recently been described that is characterized by a lack of awareness of memory impairment (i.e., anosognosia), altered functional connectivity in the brain's default mode and limbic netw ...

PERGAMON-ELSEVIER SCIENCE LTD2023

A Least-Squares Method for the Solution of the Non-smooth Prescribed Jacobian Equation

Alexandre Caboussat, Dimitrios Gourzoulidis

We consider a least-squares/relaxation finite element method for the numerical solution of the prescribed Jacobian equation. We look for its solution via a least-squares approach. We introduce a relaxation algorithm that decouples this least-squares proble ...

SPRINGER/PLENUM PUBLISHERS2022

Iterative pre-conditioning for expediting the distributed gradient-descent method: The case of linear least-squares problem

Nirupam Gupta

This paper considers the multi-agent linear least-squares problem in a server-agent network architecture. The system comprises multiple agents, each with a set of local data points. The agents are connected to a server, and there is no inter-agent communic ...

PERGAMON-ELSEVIER SCIENCE LTD2022

Eigendecomposition-Free Training of Deep Networks for Linear Least-Square Problems

Pascal Fua, Mathieu Salzmann, Fei Wang, Kwang Moo Yi, Zheng Dang, Yinlin Hu

Many classical Computer Vision problems, such as essential matrix computation and pose estimation from 3D to 2D correspondences, can be tackled by solving a linear least-square problem, which can be done by finding the eigenvector corresponding to the smal ...

IEEE COMPUTER SOC2021

Numerical Methods for First and Second Order Fully Nonlinear Partial Differential Equations

Dimitrios Gourzoulidis

This thesis focuses on the numerical analysis of partial differential equations (PDEs) with an emphasis on first and second-order fully nonlinear PDEs. The main goal is the design of numerical methods to solve a variety of equations such as orthogonal maps ...

EPFL2021

A new analysis approach for T-2 relaxometry myelin water quantification: Orthogonal Matching Pursuit

Guillaume Jean Op 't Veld

Purpose: In vivo myelin quantification can provide valuable noninvasive information on neuronal maturation and development, as well as insights into neurological disorders. Multiexponential analysis of multiecho T-2 relaxation is a powerful and widely appl ...

Wiley2019

Optimal Rates of Sketched-regularized Algorithms for Least-Squares Regression over Hilbert Spaces

Volkan Cevher, Junhong Lin

We investigate regularized algorithms combining with projection for least-squares regression problem over a Hilbert space, covering nonparametric regression over a reproducing kernel Hilbert space. We prove convergence results with respect to variants of n ...

ICML2018

Optimal Rates for Spectral Algorithms with Least-Squares Regression over Hilbert Spaces

Volkan Cevher, Junhong Lin

In this paper, we study regression problems over a separable Hilbert space with the square loss, covering non-parametric regression over a reproducing kernel Hilbert space. We investigate a class of spectral/regularized algorithms, including ridge regressi ...

2020

Neural Tanget Kernel: Convergence and Generalization in Neural Networks

Clément Hongler, Arthur Ulysse Jacot-Guillarmod, Franck Raymond Gabriel

At initialization, artificial neural networks (ANNs) are equivalent to Gaussian processes in the infinite-width limit [12, 9], thus connecting them to kernel methods. We prove that the evolution of an ANN during training can also be described by a kernel: ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2018

The effect of overall stimulus configuration on crowding

Michael Herzog, Maya Roinishvili, Mattew Pachai

The standard framework to study vision is typically hierarchical and feedforward, which facilitates deconstructing complex mechanisms into smaller, more tractable problems. However, this framework can fail when elements are presented in context, as they ar ...

2017

ALS Scheme using Extent-based Constraints for the Analysis of Chemical Reaction Systems

Michael Amrhein, Dominique Bonvin, Julien Léo Billeter

Multivariate curve resolution via alternating least squares (ALS) is used to resolve the concentration profiles C and the pure component spectra E of S species from the multivariate absorbance data A, assuming the bilinear model ...

2016

The recursive Hessian sketch for adaptive filtering

Martin Vetterli, Robin Scheibler

We introduce in this paper the recursive Hessian sketch, a new adaptive filtering algorithm based on sketching the same exponentially weighted least squares problem solved by the recursive least squares algorithm. The algorithm maintains a number of sketch ...

Ieee2016

Predicting Individual Scores From Resting State fMRI Using Partial Least Squares Regression

Stephan Morgenthaler, Djalel Eddine Meskaldji, Dimitri Nestor Alice Van De Ville, Thomas William Arthur Bolton, Maria Giulia Preti

An important question in neuroscience is to reveal the relationship between individual performance and brain activity. This could be achieved by applying model regression techniques, in which functional connectivity derived from resting-state functional ma ...

Ieee2016

Adaptive polynomial approximation by means of random discrete least squares

Giovanni Migliorati

We address adaptive multivariate polynomial approximation by means of the discrete least-squares method with random evaluations, to approximate in the L2 probability sense a smooth function depending on a random variable distributed according to a given pr ...

Springer2015

Forecasting Uncertainty in Electricity Demand

Tri Kurniawan Wijaya

Generalized Additive Models (GAM) are a widely popular class of regression models to forecast electricity demand, due to their high accuracy, flexibility and interpretability. However, the residuals of the fitted GAM are typically heteroscedastic and lepto ...

2015

Crowding and shape representations

Michael Herzog, Mauro Manassi, Aaron Michael Clarke

In crowding, target perception deteriorates when flanking elements are added. Crowding is traditionally characterized by target-flanker interactions which are (1) deleterious, (2) spatially confined within Bouma’s window, and (3) feature specific. Here, we ...

Sage Publications Ltd2015

Randomized Extended Kaczmarz For Solving Least Squares

Nikolaos Freris

We present a randomized iterative algorithm that exponentially converges in the mean square to the minimum l(2)-norm least squares solution of a given linear system of equations. The expected number of arithmetic operations required to obtain an estimate o ...

Society for Industrial and Applied Mathematics2013

An experimental design methodology to evaluate the importance of different parameters on flocculation by polyelectrolytes

David Hunkeler, Jutta Christine Wandrey, Hamideh Ahmadloo, Ineide Cruz Pinheiro

Aggregation of particles is fundamental for improving the performance of many solid/liquid processes. Aggregation can be induced by different means, and one of the most common is based on the addition of polymeric additives, namely polyelectrolytes. In thi ...

Elsevier Science Sa2013