Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

Numerous deep learning algorithms have been inspired by and understood via the notion of
information bottleneck, where unnecessary information is (often implicitly) minimized while …

Data augmentation has recently seen increased interest in graph machine learning given its
demonstrated ability to improve model performance and generalization by added training …

Recently, multivariate time series (MTS) forecasting techniques have seen rapid
development and widespread applications across various fields. Transformer-based and …

Training on large-scale graphs has achieved remarkable results in graph representation
learning, but its cost and storage have attracted increasing concerns. Existing graph …

Graph convolution networks (GCNs) for recommendations have emerged as an important
research topic due to their ability to exploit higher-order neighbors. Despite their success …

Molecular relational learning, whose goal is to learn the interaction behavior between
molecular pairs, got a surge of interest in molecular sciences due to its wide range of …

Spiking neural networks (SNNs) have garnered increased attention in the field of artificial
general intelligence (AGI) research due to their low power consumption, high computational …

3D object detection from multiple image views is a fundamental and challenging task for
visual scene understanding. However, accurately detecting objects through perspective …

In recent years, remarkable achievements have been made in artificial intelligence tasks
and applications based on deep neural networks (DNNs), especially in the fields of vision …