Theoretical guarantees for sampling and inference in generative models with latent diffusions
Theoretical guarantees for sampling and inference in generative models with latent diffusions
复制标题
具有潜在扩散的生成模型中采样和推理的理论保证
DOI：
--
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
M. Raginsky
中科院分区：
文献类型：
--
作者：
Belinda Tzen;M. Raginsky
引入并研究了一类概率生成模型，其中潜在目标是有限时间区间上的有限维扩散过程，观察变量在扩散的端点上有条件地绘制。我们的贡献如下：
We introduce and study a class of probabilistic generative models, where the latent object is a finite-dimensional diffusion process on a finite time interval and the observed variable is drawn conditionally on the terminal point of the diffusion. We make the following contributions:
We provide a unified viewpoint on both sampling and variational inference in such generative models through the lens of stochastic control.
We quantify the expressiveness of diffusion-based generative models. Specifically, we show that one can efficiently sample from a wide class of terminal target distributions by choosing the drift of the latent diffusion from the class of multilayer feedforward neural nets, with the accuracy of sampling measured by the Kullback-Leibler divergence to the target distribution.
Finally, we present and analyze a scheme for unbiased simulation of generative models with latent diffusions and provide bounds on the variance of the resulting estimators. This scheme can be implemented as a deep generative model with a random number of layers.
可解释的多项式神经常微分方程。
DOI：
10.1063/5.0130803
发表时间：
2023
期刊：
Chaos (Woodbury, N.Y.)
影响因子：
--
作者：
Fronk,Colby;Petzold,Linda
通讯作者：
Petzold,Linda