Unverified50% confidenceOpinionExact time
Among the acquisition strategies for federated active learning, disagreement-based committee query (using a federated ensemble, with each site's local model as a committee member, and the acquisition score being the variance of the predicted probabilities across sites weighted by calibration error) outperforms global entropy and local entropy strategies.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/13/2026
First Seen
Valid until: 1/11/2027
Sources
Related Entities
Related Claims
UnverifiedThe project models Tic-Tac-Toe as a supervised classification problem, with the encoded board state as input and the probability distribution of the best move among the 9 cells as output, trained using cross-entropy loss.74% similarUnverifiedMethods for overfitting benchmarks include mixing evaluation data into training corpora, indirect web-crawl contamination, instruction-style fine-tuning, and training models to recognize evaluation scenarios.65% similarUnverified标注歧义性对应机器学习中的贝叶斯最优误差率概念，可通过让多名标注者独立标注并计算标注者间Dice系数来估算性能天花板64% similarUnverifiedThe key idea of this research approach is to align the score training objective with the sampling grid, thereby jointly handling statistical estimation error and numerical discretization error within the same analytical framework.62% similarUnverifiedA middle-ground approach that teams evaluating classification requirements can adopt is: use public datasets with SFT for low-cost fine-tuning, validate the results, and then decide whether to invest more resources61% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1020476API
curl https://kongchang.com/api/v1/knowledge/claims/1020476MCP
get_claim(id=1020476)