Unverified50% confidenceFactExact time
SelectPFN is a model based on prior-data fitted networks (PFN) that can jointly perform Bayesian variable selection and prediction and is designed specifically for sparse nonlinear regression tasks; in a single forward pass it not only gives prediction results but also returns the posterior inclusion probability of each variable.
1
Sources
50%
Confidence
Long-term
Relevance
10/16/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedSelectPFN can complete a prediction in about 0.04 seconds, and the fundamental reason lies in the amortized inference nature of PFN, which amortizes the per-dataset inference cost into the pretraining stage.75% similarUnverifiedThe core idea of Prior-Fitted Networks (PFN) is to enable neural networks to directly learn to approximate the Bayesian posterior predictive distribution (PPD) by pretraining on large amounts of synthetic prior data.72% similarUnverifiedEmpirical results show that PFNs are generally able to reproduce the target posterior predictive distribution well.70% similarUnverifiedSelectPFN is pretrained in a one-off manner on synthetic tasks through a meta-prior built around a group spike-and-slab sparse input neural network.68% similarUnverified生成式模型建模联合概率 P(x,y) 或数据分布 P(x)，而判别式模型直接建模条件概率 P(y|x)；生成模型在小样本时更具优势，判别模型在大数据量时通常表现更好65% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1042160API
curl https://kongchang.com/api/v1/knowledge/claims/1042160MCP
get_claim(id=1042160)