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Learning equivariant tensor functions with applications to sparse vector
  recovery

Learning equivariant tensor functions with applications to sparse vector recovery

3 June 2024
Wilson Gregory
Josué Tonelli-Cueto
Nicholas F. Marshall
Andrew S. Lee
Soledad Villar
ArXivPDFHTML

Papers citing "Learning equivariant tensor functions with applications to sparse vector recovery"

5 / 5 papers shown
Title
Representing spherical tensors with scalar-based machine-learning models
Representing spherical tensors with scalar-based machine-learning models
Michelangelo Domina
Filippo Bigi
Paolo Pegolo
Michele Ceriotti
36
0
0
08 May 2025
Noether Networks: Meta-Learning Useful Conserved Quantities
Noether Networks: Meta-Learning Useful Conserved Quantities
Ferran Alet
Dylan D. Doblar
Allan Zhou
J. Tenenbaum
Kenji Kawaguchi
Chelsea Finn
60
26
0
06 Dec 2021
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
M. Bronstein
Joan Bruna
Taco S. Cohen
Petar Velivcković
GNN
166
1,095
0
27 Apr 2021
Invariant polynomials and machine learning
Invariant polynomials and machine learning
W. Haddadin
32
7
0
26 Apr 2021
A Practical Method for Constructing Equivariant Multilayer Perceptrons
  for Arbitrary Matrix Groups
A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups
Marc Finzi
Max Welling
A. Wilson
71
185
0
19 Apr 2021
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