Unverified50% confidenceOpinionExact time
Combining semantic distance and entropy can distinguish 'random nonsense' from 'meaningful originality': the former has large semantic distance but low effectiveness, while the latter achieves high distance and high entropy while maintaining comprehensibility.
1
Sources
50%
Confidence
Long-term
Relevance
10/21/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedThere is a weak but consistent negative correlation between split entropy and node accuracy: the higher the entropy, the more likely the node judgment is to err.79% similarUnverifiedComputing Graph Entropy typically involves constructing a k-nearest-neighbor graph and computing eigenvalues or random-walk entropy; its complexity is higher than simple distance averaging, but it is feasible on medium-sized datasets.74% similarUnverifiedThe paper finds that the commonly used Mean Pairwise Distance is not the best choice, and that Graph Entropy performs better overall.74% similarUnverified跨分词器蒸馏前应先测量熵保留率或概率质量对齐度以避免在错误目标上浪费算力73% similarUnverifiedThe concept of information entropy proposed by Shannon indicates that low-probability events carry higher information content.71% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1079463API
curl https://kongchang.com/api/v1/knowledge/claims/1079463MCP
get_claim(id=1079463)