GitHub

AKDE: Multivariate Adaptive Kernel Density Estimation Using Gaussian Mixture Model

AKDE provides an accurate, adaptive kernel density estimator based on the Gaussian Mixture Model for multidimensional data. This Python implementation includes automatic grid construction for ...
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 ...
C&EN

Efficient Implementation of Gaussian Process Regression Accelerated Saddle Point Searches with Application to Molecular Reactions

Article Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to ...
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 ...
IEEE

Gaussian Process Latent Variable Modeling for Few-Shot Time Series Forecasting

Abstract: Accurate time series forecasting is crucial for optimizing resource allocation, industrial production, and urban management, particularly with the growth of cyber-physical and IoT systems.
IEEE

Hybrid Kernel Function Enhanced Deep Gaussian Process for Wind Power Prediction

Abstract: As the global demand for renewable energy continues to grow, wind power, as a clean and sustainable form of energy, the accuracy of its generation power prediction is of great significance ...
C&EN

Applying Gaussian Process Machine Learning and Modern Probabilistic Programming to Satellite Data to Infer CO

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Satellite data provides essential insights into the spatiotemporal distribution of CO ...
Frontiers

Gaussian process latent variable models-ANN based method for automatic features selection and dimensionality reduction for control of EMG-driven systems

Electromyography (EMG) signals have gained significant attention due to their potential applications in prosthetics, rehabilitation, and human-computer interfaces. However, the dimensionality of EMG ...