Approximation Bounds for Random Neural Networks and Reservoir Systems

14 February 2020

Papers citing "Approximation Bounds for Random Neural Networks and Reservoir Systems"

31 / 31 papers shown

Title
Online Federation For Mixtures of Proprietary Agents with Black-Box Encoders Xuwei Yang Fatemeh Tavakoli D. B. Emerson Anastasis Kratsios FedML 62 0 0 30 Apr 2025
Squared families: Searching beyond regular probability models Russell Tsuchida Jiawei Liu Cheng Soon Ong Dino Sejdinovic 44 0 0 27 Mar 2025
How more data can hurt: Instability and regularization in next-generation reservoir computing Yuanzhao Zhang Edmilson Roque dos Santos Sean P. Cornelius 88 2 0 28 Jan 2025
Deep Kalman Filters Can Filter Blanka Hovart Anastasis Kratsios Yannick Limmer Xuwei Yang 53 1 0 31 Dec 2024
RandNet-Parareal: a time-parallel PDE solver using Random Neural Networks Guglielmo Gattiglio Lyudmila Grigoryeva M. Tamborrino 39 1 0 09 Nov 2024
Parallel-in-Time Solutions with Random Projection Neural Networks M. Betcke L. Kreusser Davide Murari 29 1 0 19 Aug 2024
Operator Learning Using Random Features: A Tool for Scientific Computing Nicholas H. Nelsen Andrew M. Stuart 45 12 0 12 Aug 2024
Expressivity of Neural Networks with Random Weights and Learned Biases Ezekiel Williams Avery Hee-Woon Ryoo Thomas Jiralerspong Alexandre Payeur M. Perich Luca Mazzucato Guillaume Lajoie 38 2 0 01 Jul 2024
Universal randomised signatures for generative time series modelling Francesca Biagini Lukas Gonon Niklas Walter 42 4 0 14 Jun 2024
Full error analysis of the random deep splitting method for nonlinear parabolic PDEs and PIDEs Ariel Neufeld Philipp Schmocker Sizhou Wu 45 7 0 08 May 2024
Convergence of Gradient Descent for Recurrent Neural Networks: A Nonasymptotic Analysis Semih Cayci A. Eryilmaz 26 3 0 19 Feb 2024
Approximation Rates and VC-Dimension Bounds for (P)ReLU MLP Mixture of Experts Anastasis Kratsios Haitz Sáez de Ocáriz Borde Takashi Furuya Marc T. Law MoE 41 1 0 05 Feb 2024
Unsupervised Random Quantum Networks for PDEs Josh Dees Antoine Jacquier Sylvain Laizet 21 2 0 21 Dec 2023
A Theoretical Analysis of the Test Error of Finite-Rank Kernel Ridge Regression Tin Sum Cheng Aurelien Lucchi Ivan Dokmanić Anastasis Kratsios David Belius 37 4 0 02 Oct 2023
Regret-Optimal Federated Transfer Learning for Kernel Regression with Applications in American Option Pricing Xuwei Yang Anastasis Kratsios Florian Krach Matheus Grasselli Aurelien Lucchi FedML 21 2 0 08 Sep 2023
Error Bounds for Learning with Vector-Valued Random Features S. Lanthaler Nicholas H. Nelsen 27 12 0 26 May 2023
Infinite-dimensional reservoir computing Lukas Gonon Lyudmila Grigoryeva Juan-Pablo Ortega 42 8 0 02 Apr 2023
A Brief Survey on the Approximation Theory for Sequence Modelling Hao Jiang Qianxiao Li Zhong Li Shida Wang AI4TS 30 12 0 27 Feb 2023
$Langevin dynamics based algorithm e-TH$\varepsilon$O POULA for stochastic optimization problems with discontinuous stochastic gradient$ Langevin dynamics based algorithm e-TH $\varepsilon$ O POULA for stochastic optimization problems with discontinuous stochastic gradient Dongjae Lim Ariel Neufeld Sotirios Sabanis Ying Zhang 22 6 0 24 Oct 2022
Chaotic Hedging with Iterated Integrals and Neural Networks Ariel Neufeld Philipp Schmocker 39 10 0 21 Sep 2022
Universality and approximation bounds for echo state networks with random weights Zhen Li Yunfei Yang 14 5 0 12 Jun 2022
Universal Approximation Under Constraints is Possible with Transformers Anastasis Kratsios Behnoosh Zamanlooy Tianlin Liu Ivan Dokmanić 53 26 0 07 Oct 2021
Path classification by stochastic linear recurrent neural networks Wiebke Bartolomaeus Youness Boutaib Sandra Nestler Holger Rauhut 29 3 0 06 Aug 2021
Convergence rates for shallow neural networks learned by gradient descent Alina Braun Michael Kohler S. Langer Harro Walk 22 10 0 20 Jul 2021
Non-asymptotic estimates for TUSLA algorithm for non-convex learning with applications to neural networks with ReLU activation function Dongjae Lim Ariel Neufeld Sotirios Sabanis Ying Zhang 41 19 0 19 Jul 2021
Random feature neural networks learn Black-Scholes type PDEs without curse of dimensionality Lukas Gonon 21 35 0 14 Jun 2021
Error Bounds of the Invariant Statistics in Machine Learning of Ergodic Itô Diffusions He Zhang J. Harlim Xiantao Li 18 7 0 21 May 2021
Using Echo State Networks to Approximate Value Functions for Control Allen G. Hart Kevin R. Olding Alexander M. G. Cox Olga Isupova Jonathan H.P Dawes 11 0 0 11 Feb 2021
Learning Sub-Patterns in Piecewise Continuous Functions Anastasis Kratsios Behnoosh Zamanlooy 22 10 0 29 Oct 2020
Echo State Networks trained by Tikhonov least squares are L2(μ) approximators of ergodic dynamical systems Allen G. Hart J. Hook Jonathan H.P Dawes 27 46 0 14 May 2020
$Approximation by Combinations of ReLU and Squared ReLU Ridge Functions with $ \ell^1 $ and $ \ell^0 $ Controls$ Approximation by Combinations of ReLU and Squared ReLU Ridge Functions with $\ell^1$ and $\ell^0$ Controls Jason M. Klusowski Andrew R. Barron 132 142 0 26 Jul 2016