A repo based on XiLin Li's PSGD repo that extends some of the experiments.

Pytorch implementation of KFAC and E-KFAC (Natural Gradient).

Fast and Easy Infinite Neural Networks in Python

GPU- and TPU-backed NumPy with differentiation and JIT compilation.

Source-to-Source Debuggable Derivatives in Pure Python

Source code for Naesseth et. al. "Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms" (2017)

some tools for gaussian linear dynamical systems

Efficiently computes derivatives of numpy code.

Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.

The Shogun Machine Learning Toolbox (Source Code)

code for Structured Variational Autoencoders

code for the paper "Improved Techniques for Training GANs"

Flexible Bayesian inference using TensorFlow

code for the paper "Stein Variational Gradient Descent (SVGD): A General Purpose Bayesian Inference Algorithm"

Reimplementation of DRAW

Code for reproducing key results in the paper "Improving Variational Inference with Inverse Autoregressive Flow"

TensorFlow implementation of Neural Variational Inference for Text Processing

Large-scale linear classification, regression and ranking in Python

TMAC: A Toolbox of Modern Async-Parallel, Coordinate, Splitting, and Stochastic Methods

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

Models built with TensorFlow