arXiv Machine Learning
Jun 16

A Conservation Law for Equilibrium Propagation and Coupled Learning

arXiv:2606. 15444v1 Announce Type: cross Abstract: In this paper we show that the physical learning methods known as coupled learning (CL) and equilibrium propagation (EP) conserve a mass-like quantity in the trainable parameters in the continuous-time, small-nudging limit.

By Joshua A. McGinnis, Adam G. Kline, Yoichiro Mori
arXiv AI
Jun 2

Equilibrium Propagation for Non-Conservative Systems

arXiv:2602. 03670v2 Announce Type: replace-cross Abstract: Equilibrium Propagation (EP) is a physics-inspired learning algorithm that uses stationary states of a dynamical system both for inference and learning.

By Antonino Emanuele Scurria, Dimitri Vanden Abeele, Bortolo Matteo Mognetti, Serge Massar
arXiv AI
Jun 6

Retry Policy Gradients in Continuous Action Spaces

arXiv:2606. 05888v1 Announce Type: new Abstract: Retry-based objectives such as pass@K and max@K optimize the best return obtained from multiple sampled trajectories, and recent work has shown that they can promote exploration without explicit exploration bonuses.

By Soichiro Nishimori, Paavo Parmas