Concept
Gaussian process
Related publications (32)
The very knotty lenser: Exploring the role of regularization in source and potential reconstructions using Gaussian process regression
Reconstructing lens potentials and lensed sources can easily become an underconstrained problem, even when the degrees of freedom are low, due to degeneracies, particularly when potential perturbations superimposed on a smooth lens are included. Regulariza ...
OXFORD UNIV PRESS2022Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks
Florent Gérard Krzakala, Lenka Zdeborová, Ludovic Théo Stephan, Bruno Loureiro
Despite the non-convex optimization landscape, over-parametrized shallow networks are able to achieve global convergence under gradient descent. The picture can be radically different for narrow net-works, which tend to get stuck in badly-generalizing loca ...
2022Using wavelets to capture deviations from smoothness in galaxy-scale strong lenses
Frédéric Courbin, Aymeric Alexandre Galan, Austin Chandler Peel, Georgios Vernardos
Modeling the mass distribution of galaxy-scale strong gravitational lenses is a task of increasing difficulty. The high-resolution and depth of imaging data now available render simple analytical forms ineffective at capturing lens structures spanning a la ...
EDP SCIENCES S A2022Dynamical low rank approximation for uncertainty quantification of time-dependent problems
The quantification of uncertainties can be particularly challenging for problems requiring long-time integration as the structure of the random solution might considerably change over time. In this respect, dynamical low-rank approximation (DLRA) is very a ...
EPFL2022Metric learning for kernel ridge regression: assessment of molecular similarity
Friedrich Eisenbrand, Puck Elisabeth van Gerwen, Raimon Fabregat I De Aguilar-Amat
Supervised and unsupervised kernel-based algorithms widely used in the physical sciences depend upon the notion of similarity. Their reliance on pre-defined distance metrics-e.g. the Euclidean or Manhattan distance-are problematic especially when used in c ...
IOP Publishing Ltd2022Mesoscopic modeling of hidden spiking neurons
Wulfram Gerstner, Shuqi Wang, Valentin Marc Schmutz
Can we use spiking neural networks (SNN) as generative models of multi-neuronal recordings, while taking into account that most neurons are unobserved? Modeling the unobserved neurons with large pools of hidden spiking neurons leads to severely underconstr ...
2022Micromechanically inspired investigation of cemented granular materials: part I-from X-ray micro tomography to measurable model variables
Cemented granular materials are abundant in nature and are often artificially produced. Their macroscopic behaviour is driven by small-scale material processes, which are generally classified as: grain breakage, cement damage and fragment rearrangement. Th ...
SPRINGER HEIDELBERG2022Optimal lower bounds on hitting probabilities for non-linear systems of stochastic fractional heat equations
We consider a system of d non-linear stochastic fractional heat equations in spatial dimension 1 driven by multiplicative d-dimensional space-time white noise. We establish a sharp Gaussian-type upper bound on the two-point probability density function of ...
ELSEVIER2021Deterministic error bounds for kernel-based learning techniques under bounded noise
Colin Neil Jones, Paul Scharnhorst, Emilio Maddalena
We consider the problem of reconstructing a function from a finite set of noise-corrupted samples. Two kernel algorithms are analyzed, namely kernel ridge regression and epsilon-support vector regression. By assuming the ground-truth function belongs to th ...
PERGAMON-ELSEVIER SCIENCE LTD2021