Unverified50% confidenceFactExact time
When the parameter space is generalized from finite dimensions to an infinite-dimensional function space, the inner optimization problem of maximizing the acquisition function becomes neither exhaustively searchable nor readily amenable to direct gradient descent.
1
Sources
50%
Confidence
Long-term
Relevance
10/8/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedThe functional gradient in function space is an infinite-dimensional object that cannot be precisely stored or computed in a computer, and must be approximated in practice.77% similarUnverifiedConstraint Satisfaction Problems (CSPs) reveal that a completely unconstrained search space often leads to combinatorial explosion or mediocre solutions, while moderate constraints can guide the search toward valuable regions.75% similarUnverified无梯度方法样本效率低，在高维参数空间中缺乏梯度指引意味着需要海量函数评估才能收敛75% similarUnverifiedThe limitation of specialized small models is that their generalization ability rapidly degrades when task boundaries are ambiguous, cross-domain reasoning is required, or unseen edge cases need to be handled.74% similarUnverifiedDeep learning almost always faces non-convex optimization problems, where saddle points are more common than local minima in high-dimensional parameter spaces.72% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/986386API
curl https://kongchang.com/api/v1/knowledge/claims/986386MCP
get_claim(id=986386)