Unverified50% confidenceBenchmarkExact time
The entropy loss of the EOS algorithm relative to the offline optimal solution is O(R log n), and the budget overspend is also on the order of O(R log n), where R is a drift parameter that depends on the specific instance.
1
Sources
50%
Confidence
Long-term
Relevance
10/14/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedThe binary cross-entropy loss formula is -[y·log(p) + (1-y)·log(1-p)], where y is the true label and p is the model's predicted probability for the positive class.66% similarUnverifiedThe fundamental difference between EOS and nucleus sampling and top-k sampling is that EOS does not control quality by truncating the distribution, but instead dynamically adjusts the temperature through entropy maximization under information-theoretic constraints, with guarantees from online learning theory.65% similarUnverifiedReverse-mode automatic differentiation can compute the gradients of a scalar loss with respect to all parameters in O(n) time, whereas forward mode requires O(n) forward passes65% similarUnverified对于超大规模图谱,基于 Barnes-Hut 算法可实现 O(N log N) 近似力计算以优化性能62% similarUnverifiedA model can have on the order of O(N²) parameters yet carry only on the order of O(N) cross-time state, creating a mismatch between capacity and memory.62% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1028949API
curl https://kongchang.com/api/v1/knowledge/claims/1028949MCP
get_claim(id=1028949)