Matthew Kelcey

TL;DR: The Natural Questions corpus, a question answering data set, is presented, introducing robust metrics for the purposes of evaluating question answering systems; demonstrating high human upper bounds on these metrics; and establishing baseline results using competitive methods drawn from related literature.

TL;DR: In this paper, the authors study how randomized simulated environments and domain adaptation methods can be extended to train a grasping system to grasp novel objects from raw monocular RGB images, and they extensively evaluate their approaches with a total of more than 25,000 physical test grasps, including a novel extension of pixel-level domain adaptation that they termed the GraspGAN.

TL;DR: This work study how randomized simulated environments and domain adaptation methods can be extended to train a grasping system to grasp novel objects from raw monocular RGB images, including a novel extension of pixel-level domain adaptation that is term the GraspGAN.

TL;DR: This work presents WIKIREADING, a large-scale natural language understanding task and publicly-available dataset with 18 million instances, and compares various state-of-the-art DNNbased architectures for document classification, information extraction, and question answering.

TL;DR: This article presented WikiReading, a large-scale natural language understanding task and publicly-available dataset with 18 million instances, where the task is to predict textual values from the structured knowledge base Wikidata by reading the text of the corresponding Wikipedia articles.