Learning
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)
ANDREW NG
Home page Courses
Daniel McDuff – MIT Media Lab
Gaussian Processes – slides
SIMULATION METAMODELING AND OPTIMIZATION WITH AN ADDITIVE GLOBAL AND LOCAL GAUSSIAN PROCESS MODEL FOR STOCHASTIC SYSTEMS
MENG QUN AN ADDITIVE GLOBAL AND LOCAL GAUSSIAN PROCESS MODEL FOR LARGE DATA SETS