AI & Machine Learning

Lagrangian-based Equilibrium Propagation: generalisation to arbitrary boundary conditions & equivalence with Hamiltonian Echo Learning

alinemati1983-6987Apr 14

27 sec read10 views0 listens

Researchers have developed Generalized Lagrangian Equilibrium Propagation (GLEP) to extend Equilibrium Propagation (EP) for training Energy-based Models on time-varying inputs, offering a range of new learning algorithms based on different boundary conditions. Notably, Hamiltonian Echo Learning (HEL), including its variants, emerges as the most practical instance of GLEP due to its forward-only operation and efficient scaling, making it highly suitable for hardware implementation.

Read the full article at arXiv cs.LG (ML)

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alinemati1983-6987

Lagrangian-based Equilibrium Propagation: generalisation to arbitrary boundary conditions & equivalence with Hamiltonian Echo Learning

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