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Disentangling Computation in Multi-Task Neural Networks with the Green's Operator

Paper recorded by Signals 4 on 2026-09-30 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-30 on arXiv · recorded by Signals 4 on 2026-10-01

Category: cs.LG · 机器学习 · first seen 2026-10-01

Abstract

How is computation organized and reused across tasks and time in a trained recurrent network? Most analyses emphasize the geometry of neural activity, dynamical motifs, or local perturbation growth. We instead study the network's global first-order perturbation response. The finite-horizon Green's operator maps perturbations at each source along a trajectory to their downstream state-space respons

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#8 most recent of 349 cs.LG papers we have recorded · ↑ newer: How Much Is an AI Token Worth? Scaling Laws for Wild AI-Generated Web · ↓ older: PMosFM: Preconditioned Manifold Matching for One-Step Physics-Constrai
Cite this page: Disentangling Computation in Multi-Task Neural Networks with the Green's Operator: the #8 most recent of 349 cs.LG papers we have recorded (as of 2026-09-30). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/disentangling-computation-in-multi-task-neural-networks-with-the-green-s-operato.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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