Unverified50% confidenceFactExact time
In diffusion models, the score function is used to describe the log gradient of the data distribution and is trained via denoising score matching.
1
Sources
50%
Confidence
Long-term
Relevance
10/12/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedFlow matching models generate images by learning a continuous probability flow (ODE trajectory) from the noise distribution to the data distribution, and are generally more stable to train and require fewer inference steps.77% similarUnverifiedThe key insight of this study is that when the likelihood function derived from the data model is known and differentiable, guided diffusion modeling can be used to sample the predictive distribution.76% similarUnverifiedCurrent mainstream diffusion models tend to select the highest-probability denoising path at each step, causing generated results to gravitate toward the statistical center of the training data distribution and exhibit the most averaged visual presentation.76% similarUnverified分位数回归通过直接对目标变量的条件分位数建模(如同时训练 5%、50%、95% 分位点)来得到预测中位数和区间,不需要假设误差服从高斯分布,对长尾或非对称分布更稳健。75% similarUnverifiedData mixture ratio refers to the proportion of different types of data in a pre-training dataset, and profoundly affects the distribution of a model's capabilities.75% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1012869API
curl https://kongchang.com/api/v1/knowledge/claims/1012869MCP
get_claim(id=1012869)