ESS-Flow：源空间中将免训练引导作为贝叶斯推断
ESS-Flow: Training-free guidance as Bayesian inference in source space
Adhithyan Kalaivanan, Zheng Zhao, Jens Sjölund, Fredrik Lindsten
AI 速览：ESS-Flow 利用流模型源分布的高斯先验，在源空间用椭圆切片采样做无梯度贝叶斯推断，实现预训练流模型的条件生成。
Guiding pretrained flow-based generative models for conditional generation or to produce samples with desired target properties enables solving diverse tasks without retraining on paired data. We present ESS-Flow, a gradient-free method that leverages the Gaussian prior of the source distribution in flow-based models to perform Bayesian inference directly in the source space using elliptical slice sampling. ESS-Flow only requires forward passes through the generative model and observation process, making it applicable even when gradients are unavailable, such as with non-differentiable simulation-based observations or quantization in the generation process. We demonstrate its effectiveness on scientific applications including generating materials with target properties, protein backbone structures with improved designability, and synthesizable, drug-like molecules.