REASON proposes two-loop residual analysis for learning
Original title: Robust Explanatory Analysis for Structured Optimization Networks (REASON) in Learning
The two-loop model beats the fast loop at about half the medium loop's computation, including under unseen corruptions.
REASON formulates residual-error resolution as a two-loop mechanism for structured optimization networks. A fast loop uses available evidence, while a slower loop acquires additional evidence and scores it by residual reduction. The paper says the medium-loop optimum is provably no worse over nested solution spaces, and experiments report gains under unseen corruptions and against staged classifiers and learning-to-defer methods at equal cost when a cleaner observation is available.
- The design is inspired by System 1 and System 2.
- Additional evidence is valued by residual reduction.
- The medium-loop optimum is claimed no worse than the fast loop.
- Cleaner observations beat staged classifiers at equal cost.
From the original article
1 min read at huggingface.co
We seek a hierarchical resolution of residual error under a composite learning objective as a principled mechanism for identifying and exploiting additional information, particularly under distribution shift and perturbation. Inspired by the System 1/System 2 paradigm, we formulate this principle as a two-loop mechanism serving a common inference goal.
By Vahid Jebraeeli, Hamid Krim, Keisuke Fujii · huggingface.co
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