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A theory of noisy linear recurrent neural network learning dynamics

Paper recorded by Signals 4 on 2026-10-26 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-26 on arXiv · recorded by Signals 4 on 2026-10-27

Category: cs.LG · 机器学习 · first seen 2026-10-27

Abstract

Biological neural networks are noisy: their dynamics involve difficult-to-predict fluctuations which are likely due not just to chaos, but also to a variety of low-level biophysical processes. Despite the omnipresence of intrinsic noise, its theoretical consequences are incompletely understood, especially in the context of learning. For example, does noise meaningfully shape representation learnin

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#169 most recent of 749 cs.LG papers we have recorded · ↑ newer: Selective Tie Learning: Mitigating Spurious Correlations in Preference · ↓ older: Improving Signals, Declining Encoder: A Known-Answer Test of Label-Fre
Cite this page: A theory of noisy linear recurrent neural network learning dynamics: the #169 most recent of 749 cs.LG papers we have recorded (as of 2026-10-26). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/a-theory-of-noisy-linear-recurrent-neural-network-learning-dynamics.html
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