Gaussian processes in TensorFlow
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Updated
Aug 7, 2017 - Python
Gaussian processes in TensorFlow
Implements AT-GP from Cao et. al. 2010 in GPflow
Non-stationary spectral mixture kernels implemented in GPflow
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.
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
Methods for estimating time-varying functional connectivity (TVFC)
Distributed surrogate-assisted evolutionary methods for multi-objective optimization of high-dimensional dynamical systems
Gaussian processes in TensorFlow
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