Unverified50% confidenceFactExact time
Deep neural networks contain billions or even trillions of parameters, and their black-box nature makes it difficult even for developers to accurately predict model behavior for specific inputs.
1
Sources
50%
Confidence
Long-term
Relevance
9/6/2026
First Seen
Sources
Related Entities
Related Claims
Unverified现代深度网络往往拥有数十亿乃至上万亿参数，远超训练样本数量，按经典统计学习理论应严重过拟合，实际上却展现出惊人的泛化能力79% 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75% similarUnverified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72% similarUnverifiedLarge language models are essentially deep neural networks.72% similarUnverifiedExperiments by Zhang et al. (2017) demonstrated that standard deep neural networks can perfectly memorize training data with random labels, meaning that traditional generalization bounds based on model capacity completely break down.71% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/863314API
curl https://kongchang.com/api/v1/knowledge/claims/863314MCP
get_claim(id=863314)