Gaussian processes in TensorFlow
-
Updated
Sep 27, 2024 - Python
Gaussian processes in TensorFlow
Distributed surrogate-assisted evolutionary methods for multi-objective optimization of high-dimensional dynamical systems
Jupyter Notebooks Tutorials on Gaussian Processes
Methods for estimating time-varying functional connectivity (TVFC)
LaTeX code for my PhD thesis.
Mode-constrained model-based-reinforcement learning in TensorFlow/GPflow
🤿 Implementation of doubly stochastic deep Gaussian Process using GPflow and TensorFlow 2.0
Actually Sparse Variational Gaussian Processes implemented in GPlow
Dataset and code for "Uncertainty-Informed Deep Transfer Learning of PFAS Toxicity"
Study of Gaussian Process (GP) local and global approximations, and application of the sparse GP approximation, combining both the global and local approaches.
Sparse Heteroscedastic Gaussian Processes
Subset of Data Variational Inference for Deep Gaussian Process Model
Gaussian-Processes Surrogate Optimisation in python
Bayesian Optimization using GPflow
📈 Implementation of the Graph Gaussian Process using GPflow and TensorFlow 2
Library for Deep Gaussian Processes based on GPflow
Deep convolutional gaussian processes.
Interactive Gaussian Processes
Non-stationary spectral mixture kernels implemented in GPflow
Implements AT-GP from Cao et. al. 2010 in GPflow
Add a description, image, and links to the gpflow topic page so that developers can more easily learn about it.
To associate your repository with the gpflow topic, visit your repo's landing page and select "manage topics."