Amanote Research

Amanote Research

    RegisterSign In

Towards a Topological–geometrical Theory of Group Equivariant Non-Expansive Operators for Data Analysis and Machine Learning

Nature Machine Intelligence
doi 10.1038/s42256-019-0087-3
Full Text
Open PDF
Abstract

Available in full text

Date

September 1, 2019

Authors
Mattia G. BergomiPatrizio FrosiniDaniela GiorgiNicola Quercioli
Publisher

Springer Science and Business Media LLC


Related search

Equivariant Topological Quantum Field Theory and Symmetry Protected Topological Phases

Journal of High Energy Physics
High Energy PhysicsNuclear
2017English

Designing Machine Learning Workflows With an Application to Topological Data Analysis

PLoS ONE
Multidisciplinary
2019English

Machine Learning and Data Analysis

English

Machine Learning Topological States

Physical Review B
OpticalElectronicCondensed Matter PhysicsMagnetic Materials
2017English

Statistical Machine Learning for Spectral Data Analysis

Materia Japan
2019English

Towards a Theory of Practice in Metaheuristics Design: A Machine Learning Perspective

RAIRO - Theoretical Informatics and Applications
MathematicsComputer Science ApplicationsSoftware
2006English

Task-Based Language Teaching and Expansive Learning Theory

TESL Canada Journal
2015English

Z2-Equivariant Ljusternik-Schnirelman Theory for Non-Even Functionals

Annales de l'Institut Henri Poincare. Annales: Analyse Non Lineaire/Nonlinear Analysis
Applied MathematicsMathematical PhysicsAnalysis
1998English

Machine-Learning for Cluster Analysis of Localization Microscopy Data.

2018English

Amanote Research

Note-taking for researchers

Follow Amanote

© 2025 Amaplex Software S.P.R.L. All rights reserved.

Privacy PolicyRefund Policy