Unverified50% confidenceConceptExact time
Weight space is the high-dimensional space formed by all the learnable parameters of a neural network, and the weight space of modern large models can reach dimensions on the order of a hundred billion.
1
Sources
50%
Confidence
Long-term
Relevance
11/4/2026
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69% similarUnverified乘积量化的核心思想是将高维向量空间分解为若干低维子空间，在每个子空间内独立进行码本学习与编码69% similarUnverified大模型本质上是一个由深度神经网络实现的极其复杂的函数68% similarVerifiedLatent space refers to the high-dimensional continuous vector space to which input data is mapped after being processed by the encoding layers of a neural network in deep learning, where semantically similar concepts are geometrically closer in that space.68% similarUnverifiedThe 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.68% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1135389API
curl https://kongchang.com/api/v1/knowledge/claims/1135389MCP
get_claim(id=1135389)