Graph-structured data appears frequently in domains including chemistry, natural language
semantics, social networks, and knowledge bases. In this work, we study feature learning …

Graphs are fundamental data structures which concisely capture the relational structure in
many important real-world domains, such as knowledge graphs, physical and social …

Programming is a powerful and ubiquitous problem-solving tool. Systems that can assist
programmers or even generate programs themselves could make programming more …

Artificial intelligence (AI) has undergone a renaissance recently, making major progress in
key domains such as vision, language, control, and decision-making. This has been due, in …

Language modelling provides a step towards intelligent communication systems by harnessing
large repositories of written human knowledge to better predict and understand the world…

We study characteristics of receptive fields of units in deep convolutional networks. The
receptive field size is a crucial issue in many visual tasks, as the output must respond to large …

We consider the problem of learning deep generative models from data. We formulate a
method that generates an independent sample via a single feedforward pass through a …

We investigate the problem of learning representations that are invariant to certain nuisance
or sensitive factors of variation in the data while retaining as much of the remaining …

Fundamental algorithms such as sorting or hashing are used trillions of times on any given
day 1 . As demand for computation grows, it has become critical for these algorithms to be as …

This paper addresses the challenging problem of retrieval and matching of graph structured
objects, and makes two key contributions. First, we demonstrate how Graph Neural …