Deep learning approaches to anomaly detection (AD) have recently improved the state of
the art in detection performance on complex data sets, such as large collections of images or …

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 …

This work introduces a diffusion model for molecule generation in 3D that is equivariant to
Euclidean transformations. Our E (3) Equivariant Diffusion Model (EDM) learns to denoise a …

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

This book is a foundational guide to graph representation learning, including state-of-the art
advances, and introduces the highly successful graph neural network (GNN) formalism …

This work introduces DiGress, a discrete denoising diffusion model for generating graphs
with categorical node and edge attributes. Our model utilizes a discrete diffusion process …

Predicting molecular properties with data-driven methods has drawn much attention in
recent years. Particularly, Graph Neural Networks (GNNs) have demonstrated remarkable …

Graph neural networks (GNNs) have been demonstrated to be powerful in modeling graph-
structured data. However, training GNNs requires abundant task-specific labeled data …

In this paper, we propose an end-to-end graph learning framework, namely\textbf {I}
terative\textbf {D} eep\textbf {G} raph\textbf {L} earning (\alg), for jointly and iteratively …

Contemporary neural networks still fall short of human-level generalization, which extends
far beyond our direct experiences. In this paper, we argue that the underlying cause for this …