In this tutorial, we show how we treat prompts as first-class, versioned artifacts and apply rigorous regression testing to large language model behavior using MLflow. We design an evaluation pipeline ...

Abstract: Data stream learning is an emerging machine learning paradigm designed for environments where data arrive continuously and must be processed in real time. Unlike traditional batch learning, ...

Step by step tutorial on how to make a LEGO brick vending machine using standard LEGO bricks. This video explains the full build process, including the internal layout, simple input system, and manual ...

Machine learning is transforming many scientific fields, including computational materials science. For about two decades, scientists have been using it to make accurate yet inexpensive calculations ...

Running machine learning experiments often involves a series of steps, such as data processing, training, and evaluation. Managing the dependencies and parameters for each step can become complex.

A recent study, “Picking Winners in Factorland: A Machine Learning Approach to Predicting Factor Returns,” set out to answer a critical question: Can machine learning techniques improve the prediction ...

The Perspective by Tiwary et al. (8) offers a comprehensive overview of generative AI methods in computational chemistry. Approaches that generate new outputs (e.g., inferring phase transitions) by ...

Abstract: Space-air-ground integrated networks (SAGINs) are emerging as a fundamental architecture for 6G systems to enable massive connectivity, novel applications, extreme data rates, ultra-low ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Graph neural networks for crystal property prediction typically require precise atomic ...