Diffusion models, a powerful and universal generative artificial intelligence technology, have
achieved tremendous success and opened up new possibilities in diverse applications. In …

Diffusion models, a powerful and universal generative AI technology, have achieved
tremendous success in computer vision, audio, reinforcement learning, and computational …

Diffusion models are generative models, which gradually add and remove noise to learn the
underlying distribution of training data for data generation. The components of diffusion …

Combining discrete and continuous data is an important capability for generative models.
We present Discrete Flow Models (DFMs), a new flow-based model of discrete data that …

Discrete diffusion modeling is a promising framework for modeling and generating data in
discrete spaces. To sample from these models, different strategies present trade-offs …

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 …

Discrete diffusion models have gained increasing attention for their ability to model complex
distributions with tractable sampling and inference. However, the error analysis for discrete …

Generative models on discrete state-spaces have a wide range of potential applications,
particularly in the domain of natural sciences. In continuous state-spaces, controllable and …

Random walk has wide applications in many fields, such as generative AI, biology, physics,
and chemistry. Asymmetric random walk could be described by the drift-diffusion equation …

Generative modeling via stochastic processes has led to remarkable empirical results as
well as to recent advances in their theoretical understanding. In principle, both space and …