Unverified50% confidenceSolutionExact time
When ε ≈ 1, the gradient second-moment term in the denominator is diluted, adaptive scaling has almost no effect, and the optimizer behaves close to pure SGD.
1
Sources
50%
Confidence
Long-term
Relevance
9/12/2026
First Seen
Sources
Related Entities
Related Claims
Unverified传统梯度下降方法(如SGD、Adam)要求目标函数可微且连续,在面对离散、非凸、多模态问题时容易陷入局部最优76% similarUnverifiedThe residual structure makes F=0 the default option—as long as the F(x) weights are close to zero, the residual block degenerates into an identity mapping, thereby resolving the optimization difficulty of SGD in learning identity mappings.73% similarUnverifiedsigmoid激活函数的导数最大值仅为0.25,多层连乘后导致梯度迅速消失70% similarUnverifiedThe loss curve of linear regression takes the shape of a convex bowl; convexity guarantees that optimization has a unique global minimum, and gradient descent will not get trapped in local optima.70% similarVerifiedGradient descent has three variants: batch gradient descent, stochastic gradient descent (SGD), and mini-batch gradient descent.69% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/914987API
curl https://kongchang.com/api/v1/knowledge/claims/914987MCP
get_claim(id=914987)