As ChatGPT goes viral, generative AI (AIGC, aka AI-generated content) has made headlines
everywhere because of its ability to analyze and create text, images, and beyond. With such …

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 …

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

Creating noise from data is easy; creating data from noise is generative modeling. We
present a stochastic differential equation (SDE) that smoothly transforms a complex data …

Score-based diffusion models, while achieving remarkable empirical performance, often
suffer from low sampling speed, due to extensive function evaluations needed during the …

Representing probability distributions by the gradient of their density functions has proven
effective in modeling a wide range of continuous data modalities. However, this …

Score-based generative modeling, informally referred to as diffusion models, continue to
grow in popularity across several important domains and tasks. While they provide high …

Physics-informed neural networks (PINNs) are neural networks trained by using physical
laws in the form of partial differential equations (PDEs) as soft constraints. We present a new …

Text-to-image generation (TTI) refers to the usage of models that could process text input
and generate high fidelity images based on text descriptions. Text-to-image generation …

The success of artificial intelligence (AI) in multiple disciplines and vertical domains in recent
years has promoted the evolution of mobile networking and the future Internet toward an AI …