Normalizing Flows are generative models which produce tractable distributions where both
sampling and density evaluation can be efficient and exact. The goal of this survey article is …

Decision-making based on machine learning systems, especially when this decision-making
can affect human lives, is a subject of maximum interest in the Machine Learning community …

Inspired by the human cognitive system, attention is a mechanism that imitates the human
cognitive awareness about specific information, amplifying critical details to focus more on …

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 …

By providing a simple and efficient way of computing low-variance gradients of continuous
random variables, the reparameterization trick has become the technique of choice for …

This paper presents an empirical study regarding training probabilistic neural networks
using training objectives derived from PAC-Bayes bounds. In the context of probabilistic …

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 …

This paper proposes a novel differentiable architecture search method by formulating it into
a distribution learning problem. We treat the continuously relaxed architecture mixing weight …

This modern and self-contained book offers a clear and accessible introduction to the
important topic of machine learning with neural networks. In addition to describing the …

Neural attention has become central to many state-of-the-art models in natural language
processing and related domains. Attention networks are an easy-to-train and effective …