Unverified50% confidenceFactExact time
Diffusion models, as expressive learning priors, have attracted much attention in Bayesian inverse problems.
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11/3/2026
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UnverifiedThe 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.79% similarUnverifiedIf the original simulation's assumptions about cell behavior are biased, the diffusion model will faithfully learn that bias as well.79% similarPartially VerifiedLearning diffusion models should follow the principle of first understanding the principles and thoroughly mastering the source code before talking about innovation and publishing papers; model improvements that skip hands-on source code work are not convincing.77% similarUnverified样本外测试解决的是模型是否泛化的问题，消融实验解决的是模型为什么有效的问题，两者互为补充76% similarUnverifiedAnother possibility is to rely on models with continual learning capability, enabling them to meaningfully undergo an ontological development process.76% similar
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