machine – Page 19 – machine-learning-2020

Gaussian Processes – Iterative Sparse Approximations

Lehel Csato

Efficient Reinforcement Learning using Gaussian Processes

Marc Peter Deisenroth

Approximate Methods for Propagation of Uncertainty with Gaussian Process Models

Agathe Girard

Gaussian Process Models for Robust Regression, Classification, and Reinforcement Learning

Malte Kuß

Learning with Uncertainty – Gaussian Processes and Relevance Vector Machines

Joaquin Quinonero Candela

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

Stanford University – Andrew Ng – John Duchi

CS 229 – Machine Learning – Course Materials

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)