Partially Verified65% confidenceFactTime unknown
TRE采用基于TNFA(Tagged NFA)的算法,时间复杂度为O(n·m),从根本上消除了回溯带来的指数级性能风险
3
Sources
65%
Confidence
Long-term
Relevance
6/1/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedThe self-attention mechanism resolves the vanishing gradient problem that RNN architectures face when processing long sequences and inherently supports large-scale parallel computation63% similarUnverifiedNTT (Number Theoretic Transform) can reduce the complexity of polynomial multiplication from O(n²) to O(n log n).63% similarUnverifiedThe core defect of RNNs (Recurrent Neural Networks) lies in their sequential computation nature, which requires processing sequences one time step at a time, causing long-range dependencies to decay exponentially with sequence length.61% similarUnverified精确的 K 近邻搜索(KNN)时间复杂度为 O(n×d),在数百万级别记忆条目下延迟不可接受,ANN 算法通过牺牲极小精度换取数量级速度提升61% similarUnverifiedWhen using exact rational arithmetic, the bit length of intermediate rational numbers in the classical LLL algorithm can reach O(n·β) in the worst case, with an overall complexity of O(n⁵·β³) or even higher.60% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/34132API
curl https://kongchang.com/api/v1/knowledge/claims/34132MCP
get_claim(id=34132)