Google Inc.

Like.com

Vincent Olivier Vanhoucke

Salih Burak Gokturk

Munjal Shah

Dragomir Dimitrov Anguelov

Diem Thanh Vu

Azhar H Khan

Kuang-chih Lee

Danny Yang

Lorenzo Torresani

Xiaofan Lin

Navneet Dalal

Christian Szegedy

Georg Heigold

Andrew William Senior

Michiel A U Bacchiani

Baris Sumengen

Erik Mcdermott

Ashwin Devendrappa Tengli

Sergey Ioffe

Rama Krishna Govindaraju

Nishant P Patil

Patrick An Phu Nguyen

Benoit Steiner

Julie Baumgartner

Matthew Sarett

Andrew Miller

Dan Chiao

Jacquie Phillips

Mark Moran

Jeffrey Adgate Dean

Oriol Vinyals

Johan Schalkwyk

Kurt Konolige

Mrinal Kalakrishnan

Sergey Levine

Yunfei Bai

Maria Carolina Parada San Martin

Matthieu Devin

Xin Lei

Paul Wohlhart

Mitchel Weintraub

Konstantinos Bousmalis

Julian Ibarz

Alexander Irpan

Hui Lin

Marc'aurelio Ranzato

Matthew Laurance Kelcey

Sergey Vladimir Levine

A neural network system that includes: multiple subnetworks that includes: a first subnetwork including multiple first modules, each first module including: a pass-through convolutional layer configured to process the subnetwork input for the first subnetwork to generate a pass-through output; an average pooling stack of neural network layers that collectively processes the subnetwork input for the first subnetwork to generate an average pooling output; a first stack of convolutional neural network layers configured to collectively process the subnetwork input for the first subnetwork to generate a first stack output; a second stack of convolutional neural network layers that are configured to collectively process the subnetwork input for the first subnetwork to generate a second stack output; and a concatenation layer configured to concatenate the pass-through output, the average pooling output, the first stack output, and the second stack output to generate a first module output for the first module.

Image classification neural networks

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for obtaining, by a first sequence-training speech model, a first batch of training frames that represent speech features of first training utterances; obtaining, by the first sequence-training speech model, one or more first neural network parameters; determining, by the first sequence-training speech model, one or more optimized first neural network parameters based on (i) the first batch of training frames and (ii) the one or more first neural network parameters; obtaining, by a second sequence-training speech model, a second batch of training frames that represent speech features of second training utterances; obtaining one or more second neural network parameters; and determining, by the second sequence-training speech model, one or more optimized second neural network parameters based on (i) the second batch of training frames and (ii) the one or more second neural network parameters.

Asynchronous optimization for sequence training of neural networks

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for image processing using deep neural networks. One of the methods includes receiving data characterizing an input image; processing the data characterizing the input image using a deep neural network to generate an alternative representation of the input image, wherein the deep neural network comprises a plurality of subnetworks, wherein the subnetworks are arranged in a sequence from lowest to highest, and wherein processing the data characterizing the input image using the deep neural network comprises processing the data through each of the subnetworks in the sequence; and processing the alternative representation of the input image through an output layer to generate an output from the input image.

Processing images using deep neural networks

Structured documents are processed using convolutional neural networks. For example, the processing can include receiving a rendered form of a structured document; mapping a grid of cells to the rendered form; assigning a respective numeric embedding to each cell in the grid, comprising, for each cell: identifying content in the structured document that corresponds to a portion of the rendered form that is mapped to the cell, mapping the identified content to a numeric embedding for the identified content, and assigning the numeric embedding for the identified content to the cell; generating a matrix representation of the structured document from the numeric embeddings assigned to the cells of the grids; and generating neural network features of the structured document by processing the matrix representation of the structured document through a subnetwork comprising one or more convolutional neural network layers.

Processing structured documents using convolutional neural networks

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training an action selection neural network that is used to control a robotic agent interacting with a real-world environment.

Using simulation and domain adaptation for robotic control

Growing community of inventors

San Francisco, CA, United States of America

Vincent Olivier Vanhoucke