Unverified50% confidenceFactExact time
In the DDPM reverse process, the mean μ_θ is expressed as μ_θ(x_t, t) = (1/√α_t) · (x_t - (β_t / √(1 - ᾱ_t)) · ε_θ(x_t, t)), where ε_θ is the neural network to be trained.
1
Sources
50%
Confidence
Long-term
Relevance
10/26/2026
First Seen
Sources
Diffusion Models for Beginners: From DDPM Principles to UNet in Practice
bilibili唐宇迪的AI课堂10/9/2026
Related Entities
Related Claims
UnverifiedIn the DDPM forward process, the distribution of x_t at any time step can be computed directly from x₀ and t via the formula x_t = √(ᾱ_t) · x₀ + √(1 - ᾱ_t) · ε, without step-by-step iteration.74% similarUnverifiedThe study generates samples of X_{t+Δt} conditioned on X_t, allowing the network to implicitly learn the conditional distribution p(X_{t+Δt}|X_t) by training on observed segments66% similarUnverifiedThe reverse SDE of a diffusion process can be expressed as dX_t = [f(X_t,t) - g(t)²∇log p_t(X_t)]dt + g(t)dW̄_t.66% similarUnverifiedContinuous-DTW (CDTW) re-expresses the DTW cumulative cost as a minimization problem over path integrals through a parameterizable warping function φ:[0,1]→[0,1].62% similarUnverifiedThe original DDPM sets the variance of the reverse process to a fixed value, but later improved versions allow the variance to be learned as well.61% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1101524API
curl https://kongchang.com/api/v1/knowledge/claims/1101524MCP
get_claim(id=1101524)