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Response Renormalization improves Deep Equilibrium Model training stability

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new framework called Response Renormalization to improve the training stability of Deep Equilibrium Models (DEQs). This method addresses issues with near-singular Jacobians that can lead to unreliable gradients during optimization. By selectively lifting denominators in the adjoint response, the framework aims to control amplification without excessively damping well-conditioned sensitivity, thereby making parameter updates more reliable. AI

IMPACT Enhances training stability for Deep Equilibrium Models, potentially enabling more reliable optimization in complex deep learning architectures.

RANK_REASON The cluster contains a research paper detailing a new method for improving deep learning models.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Response Renormalization improves Deep Equilibrium Model training stability

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 Español(ES) · Jose Luis Lima de Jesus Silva ·

    Response Renormalization for Critical Deep Equilibrium Models

    arXiv:2608.23725v1 Announce Type: new Abstract: Deep Equilibrium Models (DEQs) compute predictions from a hidden representation unchanged by the model update. Training through this equilibrium uses implicit differentiation and requires solving an adjoint system built from the res…

  2. arXiv cs.NE (Neural & Evolutionary) TIER_1 Español(ES) · Jose Luis Lima de Jesus Silva ·

    Response Renormalization for Critical Deep Equilibrium Models

    Deep Equilibrium Models (DEQs) compute predictions from a hidden representation unchanged by the model update. Training through this equilibrium uses implicit differentiation and requires solving an adjoint system built from the residual Jacobian. If this Jacobian is nearly singu…

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