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 ...

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 ...

In recent years, with the public availability of AI tools, more people have become aware of how closely the inner workings of ...

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

Forecasting is a fundamentally new capability that is missing from the current purview of generative AI. Here's how Kumo is changing that.

A Milton Keynes teacher has won a prestigious award for her groundbreaking new resource that aims to bring maths to life for secondary school pupils, reducing anxiety and making concepts easier to ...

Data mining is like digging for gold. You’ve likely heard the term “data mining” before, but have you wondered what it means, ...

Variational graph autoencoders (VGAEs) are popular artificial neural network (ANN)-based models for unsupervised graph representation learning tasks, including link prediction and graph generation, ...

The problem of efficient representation of graphs is a substantial challenge in graph machine learning. In this paper, we propose a novel two-stage framework for the representation of chemical ...