Deep generative models have unlocked another profound realm of human creativity. By
capturing and generalizing patterns within data, we have entered the epoch of all …

Despite a growing interest in diffusion-based language models, existing work has not shown
that these models can attain nontrivial likelihoods on standard language modeling …

Current AI-assisted protein design mainly utilizes protein sequential and structural
information. Meanwhile, there exists tremendous knowledge curated by humans in the text …

Spatio-temporal point process (STPP) is a stochastic collection of events accompanied with
time and space. Due to computational complexities, existing solutions for STPPs …

While diffusion models have achieved great success in generating continuous signals such
as images and audio, it remains elusive for diffusion models in learning discrete sequence …

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 …

Trajectory data is essential for various applications. However, publicly available trajectory
datasets remain limited in scale due to privacy concerns, which hinders the development of …

Contemporary attention-based sequential recommendations often encounter the
oversmoothing problem, which generates indistinguishable representations. Although …

The sharing of microdata, such as fund holdings and derivative instruments, by regulatory
institutions presents a unique challenge due to strict data confidentiality and privacy …