Abstract: Recently, topological graphs based on structural or functional connectivity of brain network have been utilized to construct graph neural networks (GNN) for Electroencephalogram (EEG) ...

Abstract: Missing node attributes pose a common problem in real-world graphs, impacting the performance of graph neural networks’ representation learning. Existing GNNs often struggle to effectively ...

Learn how backpropagation works by building it from scratch in Python! This tutorial explains the math, logic, and coding behind training a neural network, helping you truly understand how deep ...

The Heisenberg uncertainty principle puts a limit on how precisely we can measure certain properties of quantum objects. But researchers may have found a way to bypass this limitation using a quantum ...

STM-Graph is a Python framework for analyzing spatial-temporal urban data and doing predictions using Graph Neural Networks. It provides a complete end-to-end pipeline from raw event data to trained ...

BingoCGN employs cross-partition message quantization to summarize inter-partition message flow, which eliminates the need for irregular off-chip memory access and utilizes a fine-grained structured ...

Proceedings of The Eighth Annual Conference on Machine Learning and Systems Graph neural networks (GNNs), an emerging class of machine learning models for graphs, have gained popularity for their ...

Transition metal complexes (TMCs) are of great scientific and practical interest for applications in catalysis, biological systems, photochemistry, and sustainability, with properties highly dependent ...