Topological measurement of deep neural networks using persistent homology
type: Item
instance of: scholarly article
title: Topological measurement of deep neural networks using persistent homology
author: Satoru Watanabe
author: Hayato Yamana
published in: Annals of Mathematics and Artificial Intelligence
publication date: 2022-02-14T00:00:00Z
full work available at URL: https://arxiv.org/abs/2106.03016
Mathematics Subject Classification ID: 68T07
Mathematics Subject Classification ID: 55N31
zbMATH DE Number: 7473194
zbMATH Keywords: deep neural network
zbMATH Keywords: convolutional neural network
zbMATH Keywords: persistent homology
zbMATH Keywords: topological data analysis
describes a project that uses: Hyperopt
describes a project that uses: TensorFlow
describes a project that uses: Gudhi
describes a project that uses: MNIST
describes a project that uses: CIFAR
describes a project that uses: SVCCA
describes a project that uses: javaPlex
describes a project that uses: Python
MaRDI profile type: Publication
OpenAlex ID: W3089808853
cites work: Q4325487
cites work: The Simplex Tree: An Efficient Data Structure for General Simplicial Complexes
cites work: What can topology tell us about the neural code?
cites work: Q3655278
cites work: Topological persistence and simplification
cites work: A topological measurement of protein compressibility
cites work: Persistent homology of complex networks
cites work: Cliques and cavities in the human connectome
cites work: Q2934059
cites work: javaPlex: A Research Software Package for Persistent (Co)Homology
DOI: 10.1007/S10472-021-09761-3
description: scientific article; zbMATH DE number 7473194