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A deep learning theory for neural networks grounded in physics
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A deep learning theory for neural networks grounded in physics

B. Scellier
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Papers citing "A deep learning theory for neural networks grounded in physics"

5 / 5 papers shown
Title
Exploring bidirectional bounds for minimax-training of Energy-based models
Cong Geng
Jia Wang
Li Chen
Zhiyong Gao
J. Frellsen
Søren Hauberg
90
0
0
05 Jun 2025
Equilibrium Propagation for Learning in Lagrangian Dynamical Systems
Equilibrium Propagation for Learning in Lagrangian Dynamical Systems
Serge Massar
61
0
0
12 May 2025
Beyond backpropagation: bilevel optimization through implicit differentiation and equilibrium propagation
Beyond backpropagation: bilevel optimization through implicit differentiation and equilibrium propagation
Nicolas Zucchet
João Sacramento
87
16
0
06 May 2022
Bounds all around: training energy-based models with bidirectional bounds
Bounds all around: training energy-based models with bidirectional bounds
Cong Geng
Jia Wang
Zhiyong Gao
J. Frellsen
Søren Hauberg
75
16
0
01 Nov 2021
A contrastive rule for meta-learning
A contrastive rule for meta-learning
Nicolas Zucchet
Simon Schug
J. Oswald
Dominic Zhao
João Sacramento
MLT
118
19
0
04 Apr 2021

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