Scientific Research Publishing

Hayes, A. F. (2022). Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach (3rd ed.). The Guilford Press.

ABSTRACT: The purpose of this study was to establish the mediating role of job satisfaction (JS) in the relationship between job involvement (JI) and psychological well-being (PWB). A cross-sectional ...
marktechpost

A Coding Implementation to Establish Rigorous Prompt Versioning and Regression Testing Workflows for Large Language Models using MLflow

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 ...
IEEE

Physics-Constrained Gaussian Process Regression with Mag-Potential Kernel for Aeromagnetic Gradient Tensor Interpolation

Abstract: Aeromagnetic gradient tensor interpolation is a critical but challenging step in geophysical data processing, essential for transforming sparse, non-planar survey data into regular grids for ...
GitHub

Gap-averaged flow simulations with Gaussian Process regression.

The published wheels are currently not built with LAMMPS. Thus, running multiscale simulations with molecular dynamics is not possible with this quick installation. For the full functionality it is ...
Interesting Engineering

New adaptive system lets robots replicate human touch with far less training data

Researchers in Japan have developed an adaptive motion reproduction system that allows robots to generate human-like movements using surprisingly small amounts of training data. Despite rapid advances ...
eLife

Learning sequence-function relationships with scalable, interpretable Gaussian processes

This important work introduces a family of interpretable Gaussian process models that allows us to learn and model sequence-function relationships in biomolecules. These models are applied to three ...
Phys.org

Gaussian processes provide a new path toward quantum machine learning

Neural networks revolutionized machine learning for classical computers: self-driving cars, language translation and even artificial intelligence software were all made possible. It is no wonder, then ...