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Neural Cellular Automata Learn General Features in their Hidden Channels

Paper recorded by Signals 4 on 2026-09-18 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-18 on arXiv · recorded by Signals 4 on 2026-09-21

Category: cs.AI · 人工智能 · first seen 2026-09-21

Abstract

Modern deep learning models achieve impressive generalization through over-parameterization, but this paradigm often struggles with overfitting and memorization in few-shot regimes. Neural Cellular Automata (NCAs) offer a highly parameter-efficient alternative, yet research has focused primarily on their output, leaving the role of their internal hidden channels largely unexplored. In this paper,

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#15 most recent of 320 cs.AI papers we have recorded · ↑ newer: Benchmarking the Explanatory Quality of Open-Weight Vision-Language Mo · ↓ older: AutoRecLab: Describe the Experiment, Get the Code!
Cite this page: Neural Cellular Automata Learn General Features in their Hidden Channels: the #15 most recent of 320 cs.AI papers we have recorded (as of 2026-09-18). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/neural-cellular-automata-learn-general-features-in-their-hidden-channels.html
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