Recently, ChatGPT, along with DALL-E-2 and Codex, has been gaining significant attention
from society. As a result, many individuals have become interested in related resources and …

Deep generative models are a class of techniques that train deep neural networks to model
the distribution of training samples. Research has fragmented into various interconnected …

Denoising diffusion models (DDMs) have shown promising results in 3D point cloud
synthesis. To advance 3D DDMs and make them useful for digital artists, we require (i) high …

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 …

Score-based generative models (SGMs) have recently demonstrated impressive results in
terms of both sample quality and distribution coverage. However, they are usually applied …

Diffusion models have experienced a surge of interest as highly expressive yet efficiently
trainable probabilistic models. We show that these models are an excellent fit for …

Normalizing flows, autoregressive models, variational autoencoders (VAEs), and deep
energy-based models are among competing likelihood-based frameworks for deep …

Most spatial transcriptomics technologies are limited by their resolution, with spot sizes
larger than that of a single cell. Although joint analysis with single-cell RNA sequencing can …

Image-to-image translation (I2I) aims to transfer images from a source domain to a target
domain while preserving the content representations. I2I has drawn increasing attention and …

Anomaly detection is a crucial task for monitoring various status (ie, metrics) of entities (eg,
manufacturing systems and Internet services), which are often characterized by multivariate …