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Response Renormalization for Critical Deep Equilibrium Models
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gradient optimizationdeep learningequilibrium models
2608.23725
Builder Relevance
3h ago80%
Abstract
The paper introduces Response Renormalization to improve the reliability of gradient-based optimization in Deep Equilibrium Models by controlling near-critical adjoint amplification.
Reality Card
Core Claim
Response Renormalization effectively controls near-critical adjoint amplification, making parameter updates more reliable while preserving useful gradient information.
Method / Result
CMR and Phi-CMR yield test errors no more than five percent higher than models trained with exact implicit differentiation in over 98% of comparisons.
Limitations
The paper does not specify limitations or reproducibility concerns.
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