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Equilibrium Propagation extended to non-conservative systems

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new framework to extend Equilibrium Propagation (EP), a physics-inspired learning algorithm, to non-conservative systems. This advancement allows EP to be applied to systems with non-reciprocal interactions, which were previously a limitation. The proposed method modifies the learning dynamics to accurately compute the gradient of the cost function, enabling better performance and faster learning compared to earlier approaches. AI

RANK_REASON This is a research paper detailing a new algorithmic framework. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Equilibrium Propagation extended to non-conservative systems

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This is a research paper detailing a new algorithmic framework. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Antonino Emanuele Scurria, Dimitri Vanden Abeele, Bortolo Matteo Mognetti, Serge Massar ·

    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. In its original formulation it is limited to conservative systems, $\…

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