This paper is a broad and accessible survey of the methods we have at our disposal for
Monte Carlo gradient estimation in machine learning and across the statistical sciences: the …

Derivatives, mostly in the form of gradients and Hessians, are ubiquitous in machine
learning. Automatic differentiation (AD), also called algorithmic differentiation or simply" auto …

Currently, it is hard to reap the benefits of deep learning for Bayesian methods, which allow
the explicit specification of prior knowledge and accurately capture model uncertainty. We …

Variational autoencoders (VAEs) learn representations of data by jointly training a
probabilistic encoder and decoder network. Typically these models encode all features of …

This book is a graduate-level introduction to probabilistic programming. It not only provides a
thorough background for anyone wishing to use a probabilistic programming system, but …

We propose Edward, a Turing-complete probabilistic programming language. Edward
defines two compositional representations---random variables and inference. By treating …

Posterior inference with an intractable likelihood is becoming an increasingly common task
in scientific domains which rely on sophisticated computer simulations. Typically, these …

This paper presents a new variational inference framework for image restoration and a
convolutional neural network (CNN) structure that can solve the restoration problems …

Significant attention has recently been paid to deep learning as a method for improved
catchment modeling. Compared with process‐based models, deep learning is often …

We address the problem of 3D shape completion from sparse and noisy point clouds, a
fundamental problem in computer vision and robotics. Recent approaches are either data …