Gaussian Processes
Showing 10 of 10 projects
A Python package for constrained global optimization using Bayesian inference and Gaussian processes.
A highly efficient, scalable Gaussian process library implemented in PyTorch with GPU acceleration and modular design.
A modular library for Bayesian optimization built on PyTorch, enabling efficient optimization of expensive black-box functions.
A high-level neural network API for specifying and analyzing infinite-width neural networks as Gaussian Processes in Python.
A Common Lisp machine learning library focusing on neural networks, Boltzmann machines, and Gaussian processes with BLAS and CUDA support.
A low-level Gaussian process framework in JAX and Flax, designed for maximum flexibility and close alignment with mathematical notation.
A Julia package for Gaussian process modeling with support for exact inference, MCMC sampling, and non-Gaussian likelihoods.
A Python library for Bayesian optimization using GPflow and TensorFlow, designed for optimizing expensive black-box functions.
A Scala and JVM machine learning toolbox for research, education, and industry with an interactive REPL and end-to-end pipelines.
A JAX-based framework for approximate inference in Markov Gaussian processes using iterated Kalman smoothing.
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