Diffusion models have emerged as a powerful new family of deep generative models with
record-breaking performance in many applications, including image synthesis, video …

Schr\" odinger Bridge (SB) is an entropy-regularized optimal transport problem that has
received increasing attention in deep generative modeling for its mathematical flexibility …

Diffusion Models (DMs) have recently set state-of-the-art on many generation benchmarks.
However, there are myriad ways to describe them mathematically, which makes it difficult to …

Generative diffusion models showed high success in many fields with a powerful theoretical
background. They convert the data distribution to noise and remove the noise back to obtain …

Score-based divergences have been widely used in machine learning and statistics
applications. Despite their empirical success, a blindness problem has been observed when …

While score-based generative models (SGMs) have achieved remarkable successes in
enormous image generation tasks, their mathematical foundations are still limited. In this …

Diffusion models have swiftly taken the lead in generative modeling, establishing
unprecedented standards for producing high-quality, varied outputs. Unlike Generative …

Efficiently sampling from un-normalized target distributions is a fundamental problem in
scientific computing and machine learning. Traditional approaches such as Markov Chain …

While score-based generative models (SGMs) have achieved remarkable success in
enormous image generation tasks, their mathematical foundations are still limited. In this …

Generative adversarial networks (GANs) comprise a generator, trained to learn the
underlying distribution of the desired data, and a discriminator, trained to distinguish real …