Unverified50% confidenceFactExact time
The paper 'Mesoscopic learning dynamics of extensive-width neural networks' studies a two-layer neural network learning a target function represented by a network of the same architecture, where the network width and input dimension grow proportionally.
1
Sources
50%
Confidence
Long-term
Relevance
10/19/2026
First Seen
Sources
Related Entities
Related Claims
Unverified当需要表征的特征数量远超可用神经元数量时，网络会利用高维空间中近似正交向量数量远超维度数量的特性（Johnson-Lindenstrauss引理）将多个特征叠加压缩编码到同一组神经元上70% similarUnverifiedSince the inhabitants of Gradland are themselves neural networks, the theory is naturally compatible with deep learning models.69% similarUnverified2014 年 Yosinski 等人在论文《How transferable are features in deep neural networks?》中证明网络浅层学习的特征具有极高通用性，而深层特征逐渐变得任务特定69% similarUnverifiedLearning DeepAgents requires focusing on filling in LangGraph's state flow and branch control mechanisms, as well as the creation and scheduling patterns of sub-agents.68% similarUnverifiedModern deep learning frameworks PyTorch and TensorFlow represent neural networks as computational graphs and compute gradients in reverse via automatic differentiation (Autograd).67% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1064963API
curl https://kongchang.com/api/v1/knowledge/claims/1064963MCP
get_claim(id=1064963)