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SPEEDING UP KRYLOV SUBSPACE METHODS FOR COMPUTING f(A)b VIA RANDOMIZATION
Daniel Kressner, Alice Cortinovis
This work is concerned with the computation of the action of a matrix function f(A), such as the matrix exponential or the matrix square root, on a vector b. For a general matrix A, this can be done by computing the compression of A onto a suitable Krylov ...
Siam Publications2024Towards Trustworthy Deep Learning for Image Reconstruction
The remarkable ability of deep learning (DL) models to approximate high-dimensional functions from samples has sparked a revolution across numerous scientific and industrial domains that cannot be overemphasized. In sensitive applications, the good perform ...
EPFL2024Generalization of Scaled Deep ResNets in the Mean-Field Regime
Volkan Cevher, Grigorios Chrysos, Fanghui Liu
Despite the widespread empirical success of ResNet, the generalization properties of deep ResNet are rarely explored beyond the lazy training regime. In this work, we investigate scaled ResNet in the limit of infinitely deep and wide neural networks, of wh ...
2024Partial discharge localization in power transformer tanks using machine learning methods
Marcos Rubinstein, Hamidreza Karami
This paper presents a comparison of machine learning (ML) methods used for three-dimensional localization of partial discharges (PD) in a power transformer tank. The study examines ML and deep learning (DL) methods, ranging from support vector machines (SV ...
2024Gibbs sampling the posterior of neural networks
Lenka Zdeborová, Giovanni Piccioli, Emanuele Troiani
In this paper, we study sampling from a posterior derived from a neural network. We propose a new probabilistic model consisting of adding noise at every pre- and post-activation in the network, arguing that the resulting posterior can be sampled using an ...
Bristol2024BENIGN LANDSCAPES OF LOW-DIMENSIONAL RELAXATIONS FOR ORTHOGONAL SYNCHRONIZATION ON GENERAL GRAPHS
Orthogonal group synchronization is the problem of estimating n elements Z(1),& mldr;,Z(n) from the rxr orthogonal group given some relative measurements R-ij approximate to Z(i)Z(j)(-1). The least-squares formulation is nonconvex. To avoid its local minim ...
Siam Publications2024A ride time-oriented scheduling algorithm for dial-a-ride problems
Nikolaos Geroliminis, Claudia Bongiovanni, Mor Kaspi
This paper offers a new algorithm to efficiently optimize scheduling decisions for dial-a-ride problems (DARPs), including problem variants considering electric and autonomous vehicles (e-ADARPs). The scheduling heuristic, based on linear programming theor ...
Pergamon-Elsevier Science Ltd2024A graph convolutional autoencoder approach to model order reduction for parametrized PDEs
Jan Sickmann Hesthaven, Federico Pichi
The present work proposes a framework for nonlinear model order reduction based on a Graph Convolutional Autoencoder (GCA-ROM). In the reduced order modeling (ROM) context, one is interested in obtaining real -time and many-query evaluations of parametric ...
San Diego2024Advancing Self-Supervised Deep Learning for 3D Scene Understanding
Recent advancements in deep learning have revolutionized 3D computer vision, enabling the extraction of intricate 3D information from 2D images and video sequences. This thesis explores the application of deep learning in three crucial challenges of 3D com ...
EPFL2024The Power of Two Matrices in Spectral Algorithms for Community Recovery
Spectral algorithms are some of the main tools in optimization and inference problems on graphs. Typically, the graph is encoded as a matrix and eigenvectors and eigenvalues of the matrix are then used to solve the given graph problem. Spectral algorithms ...
Ieee-Inst Electrical Electronics Engineers Inc2024