Compact representation of graph data is a fundamental problem in pattern recognition and machine learning area. Recently, graph neural networks (GNNs) have been widely studied for graph-structured ...

Isolation Forest detects anomalies by isolating observations. It builds binary trees (called iTrees) by recursively ...

Graph Neural Networks (GNNs) have been proposed to learn graph representations for various graph mining tasks such as link prediction and node classification. These methods aggregate information from ...

New research reveals a surprising geometric link between human and machine learning. A mathematical property called convexity ...

To flexibly and robustly handle diverse problems, AI systems can leverage dual-process theories of human cognition that ...