Unverified50% confidenceFactExact time
In recurrent neural networks, the critical state corresponds to the maximum singular value (spectral radius) of the Jacobian matrix approaching 1.
1
Sources
50%
Confidence
Long-term
Relevance
10/8/2026
First Seen
Sources
Related Entities
Related Claims
Unverified线性代数中的矩阵分解与特征值分解对应神经网络的权重空间分析77% similarUnverifiedOrthogonal polynomials are typically defined on bounded or semi-infinite intervals, whereas real inputs to neural networks are often unbounded real values, creating a gap between the two.71% similarUnverifiedFor each generator, the key neurons consistently selected account for less than 1% of the total, and they are highly consistent across different cross-validation folds and random seeds.69% similarUnverifiedThe universal approximation theorem for neural networks states that a sufficiently wide or sufficiently deep network can approximate any continuous function, but does not answer the question of what network size is required to achieve a specific accuracy.67% similarUnverified叠加现象指神经网络倾向于用远少于特征数量的神经元同时编码大量语义特征，与传统一个神经元一个特征的直觉相悖67% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/986789API
curl https://kongchang.com/api/v1/knowledge/claims/986789MCP
get_claim(id=986789)