Diffusion models have achieved great success in modeling continuous data modalities such
as images, audio, and video, but have seen limited use in discrete domains such as …

Despite their groundbreaking performance for many generative modeling tasks, diffusion
models have fallen short on discrete data domains such as natural language. Crucially …

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 …

Non-autoregressive (NAR) text generation has attracted much attention in the field of natural
language processing, which greatly reduces the inference latency but has to sacrifice the …

Diffusion models that are based on iterative denoising have been recently proposed and
leveraged in various generation tasks like image generation. Whereas, as a way inherently …

Diffusion models (DM) can gradually learn to remove noise, which have been widely used in
artificial intelligence generated content (AIGC) in recent years. The property of DM for …

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 …

The recent surge of generative AI has been fueled by the generative power of diffusion
probabilistic models and the scalable capabilities of large language models. Despite their …

Recently, continuous diffusion models (CDM) have been introduced into non-autoregressive
(NAR) text-to-text generation. However, the discrete nature of text increases the difficulty of …

Diffusion models (DM) can gradually learn to re-move noise, which have been widely used
in artificial intelligence generated content (AIGC) in recent years. The property of DM for …