Google Inc.

Amr Ahmed

Alexander Johannes Smola

Andrew Stephen Tomkins

Eugene Jon Shekita

Marc Alexander Najork

Vanja Josifovski

Jie Yang

Luis Garcia Pueyo

Mike Bendersky

MyLinh Yang

Amitabh Saikia

Hui Tan

Daniel Joseph Clancy

Marc-Allen Cartright

Manzil Zaheer

Mu Li

Daniel Wyatt

Bor-Yiing Su

Martin Zinkevich

Guru Guruganesh

Weinan Zhang

Joshua Timothy Ainslie

Kumar Avinava Dubey

Santiago Ontañón

Philip Pham

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing network inputs using an attention neural network that has one or more sparse attention sub-layers. Each sparse attention sub-layer is configured to apply a sparse attention mechanism that attends differently for input positions that are in a first proper subset of the input positions in the input to the sub-layer than for positions that are not in the first proper subset.

Attention neural networks with sparse attention mechanisms

Methods and apparatus related to determining an activity of a user based on sensor readings from sensor(s), and providing, for presentation to the user via a user interface output of a computing device of the user, information that is based on the determined activity. In some implementations, the information may be provided in response to input entered by the user via a user interface input device of the computing device of the user. In some implementations, the input may be a search query and the information may be search results. In some implementations, the input may be a partial query and the information may be query suggestions.

Providing query suggestions and/or search results based on determined user activity

Methods, apparatus, and computer-readable media are provided for analyzing a cluster of communications, such as B2C emails, to generate a template for the cluster that defines transient segments and fixed segments of the cluster of communications. More particularly, methods, apparatus, and computer-readable media are provided for generating and/or applying a trained structured machine learning model for a generated template that can be used to determine, for one or more transient segments of subsequent communications, a corresponding probability that a given semantic label is the correct semantic label for extracted content of the transient segment(s).

Generating and applying a trained structured machine learning model for determining a semantic label for content of a transient segment of a communication

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for handling faults in a distributed key-value storage system. One of the methods includes receiving an indication that a machine storing a primary replica of a first replication chain is inactive, in response to receiving the indication, generating a concatenated replica comprising a first replica of the first replication chain and a second replica of a second replication chain, the second replication chain comprising replicas of a second key segment, the second key segment being adjacent to the first key segment in the multiple key segments of the plurality of keys, and providing, to another machine in the ordered sequence of machines, a notification of availability of the concatenated replica.

Fault tolerant distributed key-value storage

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Mountain View, CA, United States of America

Amr Ahmed