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autograd

Efficiently computes derivatives of numpy code.

deep_gp_random_features

deepcgp

Deep convolutional gaussian processes.

doubly-stochastic-dgp

Deep Gaussian Processes with Doubly Stochastic Variational Inference

dropoutuncertaintyexps

Experiments used in "Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning"

gp-demo

hamiltonian-monte-carlo

Implementation of Hamiltonian Monte Carlo using Google's TensorFlow

mcmc-demo

Interactive Markov-chain Monte Carlo javascript demo

ml-videos

A collection of video resources for machine learning

neural-processes

nngp

Deep neural network kernel for Gaussian process

one-hundred-layers-tiramisu

Keras Implementation of The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation by (Simon Jégou, Michal Drozdzal, David Vazquez, Adriana Romero, Yoshua Bengio)

page

probabilistic-backpropagation

Implementation in C and Theano of the method Probabilistic Backpropagation for scalable Bayesian inference in deep neural networks.

sgld