LaTeX code for my PhD thesis.
-
Updated
Apr 19, 2024 - TeX
LaTeX code for my PhD thesis.
Gaussian processes in TensorFlow
Towards GPflow 1.0
Implementation of the COGP model
Implements AT-GP from Cao et. al. 2010 in GPflow
Study of Gaussian Process (GP) local and global approximations, and application of the sparse GP approximation, combining both the global and local approaches.
Mode-constrained model-based-reinforcement learning in TensorFlow/GPflow
Subset of Data Variational Inference for Deep Gaussian Process Model
Sparse Heteroscedastic Gaussian Processes
Distributed surrogate-assisted evolutionary methods for multi-objective optimization of high-dimensional dynamical systems
Methods for estimating time-varying functional connectivity (TVFC)
Actually Sparse Variational Gaussian Processes implemented in GPlow
Jupyter Notebooks Tutorials on Gaussian Processes
📈 Implementation of the Graph Gaussian Process using GPflow and TensorFlow 2
Interactive Gaussian Processes
Dataset and code for "Uncertainty-Informed Deep Transfer Learning of PFAS Toxicity"
Library for Deep Gaussian Processes based on GPflow
Gaussian-Processes Surrogate Optimisation in python
🤿 Implementation of doubly stochastic deep Gaussian Process using GPflow and TensorFlow 2.0
Non-stationary spectral mixture kernels implemented 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."