A homotopy training algorithm for fully connected neural networks

DOI10.1098/rspa.2019.0662zbMath1472.68131arXiv1903.09872OpenAlexW3103609633WikidataQ91866738 ScholiaQ91866738MaRDI QIDQ5160822

Wenrui Hao, Qipin Chen

Publication date: 29 October 2021

Published in: Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1903.09872

zbMATH Keywords

neural networkhomotopy methodartificial intelligencecomputational mathematicsmachine learningtraining algorithm

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (4)

Greedy training algorithms for neural networks and applications to PDEs ⋮ A gradient descent method for solving a system of nonlinear equations ⋮ A weight initialization based on the linear product structure for neural networks ⋮ A stochastic homotopy tracking algorithm for parametric systems of nonlinear equations

Cites Work

• Unnamed Item
• Unnamed Item
• A homotopy method based on WENO schemes for solving steady state problems of hyperbolic conservation laws
• Bifurcation for a free boundary problem modeling the growth of a tumor with a necrotic core
• A three-dimensional steady-state tumor system
• Efficient path tracking methods
• Computing all solutions to polynomial systems using homotopy continuation
• Multilayer feedforward networks are universal approximators
• A homotopy method for parameter estimation of nonlinear differential equations with multiple optima
• Quantifying predictability through information theory: small sample estimation in a non-Gaussian framework
• An equation-by-equation method for solving the multidimensional moment constrained maximum entropy problem
• Parameter estimation of social forces in pedestrian dynamics models via a probabilistic method
• Mathematical model of sarcoidosis
• Singular solutions, repeated roots and completeness for higher-spin chains
• Adaptive Multiprecision Path Tracking
• Two-Level Spectral Methods for Nonlinear Elliptic Equations with Multiple Solutions
• Optimization Methods for Large-Scale Machine Learning
• Convergence of a homotopy finite element method for computing steady states of Burgers’ equation
• BinaryRelax: A Relaxation Approach for Training Deep Neural Networks with Quantized Weights

This page was built for publication: A homotopy training algorithm for fully connected neural networks