Learning useful representations with little or no supervision is a key challenge in artificial
intelligence. We provide an in-depth review of recent advances in representation learning …

Recently, machine/deep learning techniques are achieving remarkable success in a variety
of intelligent control and management systems, promising to change the future of artificial …

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 …

Neural compression is the application of neural networks and other machine learning
methods to data compression. Recent advances in statistical machine learning have opened …

Generative deep machine learning models now rival traditional quantum‐mechanical
computations in predicting properties of new structures, and they come with a significantly …

Compression and computational efficiency in deep learning have become a problem of
great significance. In this work, we argue that the most principled and effective way to attack …

To measure how well pretrained representations encode some linguistic property, it is
common to use accuracy of a probe, ie a classifier trained to predict the property from the …

In this paper we present aa deep generative model for lossy video compression. We employ
a model that consists of a 3D autoencoder with a discrete latent space and an …

The success of deep learning in numerous application domains created the de-sire to run
and train them on mobile devices. This however, conflicts with their computationally, memory …

Variational methods have been previously explored as a tractable approximation to
Bayesian inference for neural networks. However the approaches proposed so far have only …