Gaussian processes in TensorFlow
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Updated
Aug 7, 2017 - Python
Gaussian processes in TensorFlow
Towards GPflow 1.0
Implementation of the COGP model
Implements AT-GP from Cao et. al. 2010 in GPflow
Non-stationary spectral mixture kernels implemented in GPflow
Interactive Gaussian Processes
Deep convolutional gaussian processes.
Library for Deep Gaussian Processes based on GPflow
📈 Implementation of the Graph Gaussian Process using GPflow and TensorFlow 2
Bayesian Optimization using GPflow
Gaussian-Processes Surrogate Optimisation in python
Subset of Data Variational Inference for Deep Gaussian Process Model
Sparse Heteroscedastic Gaussian Processes
Study of Gaussian Process (GP) local and global approximations, and application of the sparse GP approximation, combining both the global and local approaches.
Dataset and code for "Uncertainty-Informed Deep Transfer Learning of PFAS Toxicity"
Actually Sparse Variational Gaussian Processes implemented in GPlow
🤿 Implementation of doubly stochastic deep Gaussian Process using GPflow and TensorFlow 2.0
Mode-constrained model-based-reinforcement learning in TensorFlow/GPflow
LaTeX code for my PhD thesis.
Methods for estimating time-varying functional connectivity (TVFC)
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