Jointly-trained tree kernels for Gaussian processes
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Updated
May 12, 2024 - Python
Jointly-trained tree kernels for Gaussian processes
American Sign Language (ASL) Detection using CNN
A simple tool to help you with Gaussian calculations
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
Software Suite for Sensor Placement and Informative Path Planning
A highly efficient implementation of Gaussian Processes in PyTorch
Estimate Realtime Case Counts and Time-varying Epidemiological Parameters
A framework for statistical modelling in C++.
Summary notebooks using derivative gaussian processes with tinygp. We implement a 2D derivative gaussian process and successfully use derivatives to regularize SVI fits with a gaussian process model..
Julia package for kernel functions for machine learning
Reproducible code for our paper, "On Causal Discovery with Convergent Cross Mapping"
Fast & scalable MCMC for all your exoplanet needs!
Will be a Gaussian Process library, aiming to cover regression, classification and latent space modelling.
Gaussian processes in TensorFlow
Gaussian process regression
Geostatistical processes for the GeoStats.jl framework
Combining tree-boosting with Gaussian process and mixed effects models
Efficient global optimization toolbox in Rust: bayesian optimization, mixture of gaussian processes, sampling methods
Simple Time Series Modelling Using Gaussian Processes
Includes codes for the forthcoming paper, "Learning to generate synthetic human mobility data: A physics-regularized Gaussian process approach based on multiple kernel learning"
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