Unverified50% confidenceFactExact time
Fitting the parameters of a four-layer neural network is, in some instances, hard enough under worst-case complexity to change our understanding of cryptography.
1
Sources
50%
Confidence
Long-term
Relevance
11/3/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedEnd-to-end neural network methods can flexibly learn the hierarchical structure, but it is very difficult to give bounds on the generalization error, making it hard in practice to judge when the model fails.73% similarUnverifiedThe core difficulty of approximating deterministic logic with neural networks is that deterministic logic requires step-by-step progression with zero errors, whereas neural networks are inherently continuous and approximate and susceptible to the accumulation of numerical errors, so small errors are amplified during long-sequence execution.70% similarUnverifiedThe core challenges that coding and cybersecurity pose to large models include long-context understanding, precise symbolic reasoning, and an extremely low tolerance for hallucination.69% similarUnverifiedThe end-to-end neural network separation approach EEND jointly optimizes separation and recognition, but still faces significant challenges in scenarios where more than 4 people speak simultaneously.69% similarUnverified神经网络存在对抗样本脆弱性，微小的人类不可察觉的输入扰动可能导致模型输出剧烈变化69% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1132194API
curl https://kongchang.com/api/v1/knowledge/claims/1132194MCP
get_claim(id=1132194)