Unverified50% confidenceBenchmarkExact time
On nonlinear designs beyond the prior range with p up to 500, SelectPFN's area under the precision-recall curve (AUPRC) improved by 0.23-0.28 compared with lasso, BART, DART, LassoNet, and group-penalized sparse neural networks.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/16/2026
First Seen
Valid until: 1/14/2027
Sources
Related Entities
Related Claims
Unverified神经网络中各层对精度损失的敏感程度差异显著,patch projection等输入投影层敏感度高,中间主干的注意力与FFN模块对低精度更鲁棒63% similarUnverifiedOn the algorithm engineering task (Lasso path solver), Dream-RSI reduces the number of agent calls by 162× compared to baselines such as SimpleTES while still outperforming standard library implementations.63% similarUnverifiedKalman-type methods assume that both state and observation errors follow Gaussian distributions and perform well in linear systems, but their accuracy degrades when faced with high-dimensional non-Gaussian problems.62% similarVerifiedApproximate nearest neighbor (ANN) algorithms such as HNSW and IVF achieve order-of-magnitude speed improvements by sacrificing a small amount of precision.62% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1042168API
curl https://kongchang.com/api/v1/knowledge/claims/1042168MCP
get_claim(id=1042168)