Abstract: The excellent performance of graph neural networks (GNNs), which learn node representations by aggregating their neighborhood information, led to their use in various graph tasks. However, ...

Like this? Get more RAIL delivered to your browser! Click here to add RAIL as a preferred source on Google. Operational changes and tactical lower-value investments form the focus of Network Rail’s ...

Eric Gutiérrez, 6th February 2026. A Python implementation of a 1-hidden layer neural network built entirely from first principles. This project avoids deep learning libraries (like TensorFlow or ...

Abstract: Large language models (LLMs) have transformed code generation across various fields. Here, we study the specific opportunities and challenges that LLMs present in generating hardware designs ...

3D rendering—the process of converting three-dimensional models into two-dimensional images—is a foundational technology in computer graphics, widely used across gaming, film, virtual reality, and ...

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 ...

With increasing model complexity, models are typically re-used and evolved rather than starting from scratch. There is also a growing challenge in ensuring that these models can seamlessly work across ...