Smarter applications are making better use of the insights gleaned from data, having an
impact on every industry and research discipline. At the core of this revolution lies the tools …

Many modern unsupervised or semi-supervised machine learning algorithms rely on
Bayesian probabilistic models. These models are usually intractable and thus require …

Over the last decade, neural networks have reached almost every field of science and
become a crucial part of various real world applications. Due to the increasing spread …

While conceptually simple, Bayesian methods can be mathematically and numerically
challenging. Probabilistic programming languages (PPLs) implement functions to easily …

We propose a new regularization method based on virtual adversarial loss: a new measure
of local smoothness of the conditional label distribution given input. Virtual adversarial loss …

An ever-increasing share of human interaction, communication, and culture is recorded as
digital text. We provide an introduction to the use of text as an input to economic research …

Creating autonomous robots that can actively explore the environment, acquire knowledge
and learn skills continuously is the ultimate achievement envisioned in cognitive and …

Learning individual-level causal effects from observational data, such as inferring the most
effective medication for a specific patient, is a problem of growing importance for policy …

The TensorFlow Distributions library implements a vision of probability theory adapted to the
modern deep-learning paradigm of end-to-end differentiable computation. Building on two …

The original" Seven Motifs" set forth a roadmap of essential methods for the field of scientific
computing, where a motif is an algorithmic method that captures a pattern of computation …