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Found 5 papers, 2 papers with code
Faster variational inducing input Gaussian process classification
However, the new lower bound depends on $O(m^2)$ variational parameter, which makes optimization challenging in case of big m. In this work we develop a new approach for training inducing input GP models for classification problems.
Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition
We propose a method (TT-GP) for approximate inference in Gaussian Process (GP) models.
Hamiltonian Monte-Carlo for Orthogonal Matrices
In \citet{byrne2013geodesic} authors have already considered sampling from distributions over manifolds using exact geodesic flows in a scheme similar to Hamiltonian Monte Carlo (HMC).
MARS: Masked Automatic Ranks Selection in Tensor Decompositions
Tensor decomposition methods have proven effective in various applications, including compression and acceleration of neural networks.
Machine Learning Methods for Spectral Efficiency Prediction in Massive MIMO Systems
Channel decoding, channel detection, channel assessment, and resource management for wireless multiple-input multiple-output (MIMO) systems are all examples of problems where machine learning (ML) can be successfully applied.
