Variational autoencoders (VAEs) are powerful deep generative models widely used to
represent high-dimensional complex data through a low-dimensional latent space learned …

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 …

We study the problem of efficient semantic segmentation for large-scale 3D point clouds. By
relying on expensive sampling techniques or computationally heavy pre/post-processing …

An Introduction to Variational Autoencoders Page 1 An Introduction to Variational Autoencoders
Page 2 Other titles in Foundations and Trends R in Machine Learning Computational Optimal …

Generative flow networks (GFlowNets) are a method for learning a stochastic policy for
generating compositional objects, such as graphs or strings, from a given unnormalized …

We study the problem of efficient semantic segmentation of large-scale 3D point clouds. By
relying on expensive sampling techniques or computationally heavy pre/post-processing …

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 …

Topic models extract groups of words from documents, whose interpretation as a topic
hopefully allows for a better understanding of the data. However, the resulting word groups …

Centralised training with decentralised execution is an important setting for cooperative
deep multi-agent reinforcement learning due to communication constraints during execution …

Learning useful representations without supervision remains a key challenge in machine
learning. In this paper, we propose a simple yet powerful generative model that learns such …