Joaquin Quiñonero Candela

Director of Applied Machine Learning at Facebook Approximation Methods for Gaussian Process Regression Proceedings of Machine Learning Research – Volume 1: Gaussian Processes in Practice, 12-13 June 2006, Bletchley Park, UK Incremental Gaussian Processes  

Durk Kingma

 Machine Learning Research Scientist @ OpenAI   

Probabilistic Feature Learning Using Gaussian Process Auto-Encoders

  Simon Olofson – PhD Thesis Reference from PhD Thesis: Auto-Encoding Variational Bayes Stochastic Backpropagation and Approximate Inference in Deep Generative Models Generalized Product of Experts for Automatic and Principled Fusion of Gaussian Process Predictions From Pixels to Torques: Policy Learning with Deep Dynamical Models Sparse Greedy Gaussian Process Regression Autoencoders, Unsupervised Learning, and Deep … Read more

Melih Kandemir

Özyeğin University Bayesian Modeling and Inference Course Gaussian Processes for Machine Learning Heidelberg Collaboratory for Image Processing Asymmetric Transfer Learning with Deep Gaussian Processes (video)  

Videolectures

Wikipedia : VideoLectures.net Videolectures home page Videolectures : Gaussian Process  

Gaussian Process : Deep Gaussian Process – Warped Gaussian Process -Additive Kernel

The Deep Feed-Forward Gaussian Process: An Effective Generalization to Covariance Priors Warped Gaussian Processes Occupancy Mapping with Uncertain Inputs Warped Gaussian Processes Manifold Gaussian Processes for Regression Sparse Gaussian Processes using Pseudo-inputs Deep Gaussian Processes Chained Gaussian Processes Student-t Processes as Alternatives to Gaussian Processe Introduction to Gaussian Process Additive Gaussian Processes ACCURACY VERSUS INTERPRETABILITY … Read more