ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2202.00451
  4. Cited By
GENEOnet: A new machine learning paradigm based on Group Equivariant
  Non-Expansive Operators. An application to protein pocket detection

GENEOnet: A new machine learning paradigm based on Group Equivariant Non-Expansive Operators. An application to protein pocket detection

31 January 2022
Giovanni Bocchi
Patrizio Frosini
Alessandra Micheletti
A. Pedretti
Carmen Gratteri
Filippo Lunghini
A. Beccari
Carmine Talarico
ArXivPDFHTML

Papers citing "GENEOnet: A new machine learning paradigm based on Group Equivariant Non-Expansive Operators. An application to protein pocket detection"

2 / 2 papers shown
Title
A topological model for partial equivariance in deep learning and data
  analysis
A topological model for partial equivariance in deep learning and data analysis
L. Ferrari
Patrizio Frosini
Nicola Quercioli
Francesca Tombari
24
2
0
25 Aug 2023
Low-Resource White-Box Semantic Segmentation of Supporting Towers on 3D
  Point Clouds via Signature Shape Identification
Low-Resource White-Box Semantic Segmentation of Supporting Towers on 3D Point Clouds via Signature Shape Identification
Diogo Mateus Lavado
Cláudia Soares
Alessandra Micheletti
Giovanni Bocchi
Alex Coronati
Manuel F. Silva
Patrizio Frosini
3DPC
30
2
0
13 Jun 2023
1