Abstract: This tutorial aims to provide an intuitive introduction to Gaussian process regression (GPR). GPR models have been widely used in machine learning applications due to their representation ...

A from-scratch PyTorch implementation of TurboQuant (ICLR 2026), Google's two-stage vector quantization algorithm for compressing LLM key-value caches — enhanced ...

Abstract: Compressive sensing (CS) has become a popular signal processing technique and has extensive applications in numerous fields such as wireless communications, image processing, magnetic ...

Data clustering is the process of grouping data items so that similar items are placed in the same cluster. There are several different clustering techniques, and each technique has many variations.

GPR works well with small datasets and generates a metric of confidence of a predicted result, but it's moderately complex and the results are not easily interpretable, says Dr. James McCaffrey of ...

Landslide hazards are complex nonlinear systems with a highly dynamic nature. Accurate forecasting of landslide displacement and evolution is crucial for the prevention and mitigation of landslide ...

Molecular absorbance and fluorescence measurements are usually performed by benchtop or portable USB spectrophotometers or fluorometers. However, even the simplest configuration of these instruments ...

Department of Physics and Warwick Centre for Predictive Modelling, School of Engineering, University of Warwick, Coventry CV4 7AL, United Kingdom Laboratory of Computational Science and Modeling, IMX, ...

MuyGPyS is a general-purpose Gaussian process library, similar to GPy, GPyTorch, or GPflow. MuyGPyS differs from the other options in that it constructs approximate GP models using nearest neighbors ...