Google Inc.

David Martin Dohan

Quoc V Le

Nathan Silberman

Dilip Krishnan

Dengyong Zhou

Dumitru Erhan

Konstantinos Bousmalis

David J Bieber

Ed Huai-Hsin Chi

David Richard So

Aakanksha Chowdhery

Augustus Quadrozzi Odena

Jacob Austin

Maarten Paul Bosma

Chen Liang

Dale Eric Schuurmans

Jason Weng Wei

Olivier Jean Andrè Bousquet

Le Hou

Charles Aloysius Sutton

Guy Gur-Ari Krakover

Jiageng Luan

Anders Johan Andreassen

Xuezhi Wang

Henryk Michalewski

Aitor Lewkowycz

Nathanael Schärli

Maxwell Nye

Sharan Narang

Nathan Scales

An example technique for image analysis is provided. An example image analysis method includes obtaining an instructive sequence descriptive of an instructive query, an instructive response, and an instructive trace of intermediate states from the instructive query to the instructive response. The example image analysis method includes inputting, to a machine-learned model, the instructive sequence and an operative image processing query that comprises image data, wherein the machine-learned model is configured to process the operative query with attention over the instructive sequence. The example method can include generating, using the machine-learned model and responsive to the operative query, an operative image processing response that comprises an analysis of the image data.

Image analysis by prompting of machine-learned models using chain of thought

A method for receiving training data for training a neural network to perform a machine learning task and for searching for, using the training data, an optimized neural network architecture for performing the machine learning task is described. Searching for the optimized neural network architecture includes: maintaining population data; maintaining threshold data; and repeatedly performing the following operations: selecting one or more candidate architectures from the population data; generating a new architecture from the one or more selected candidate architectures; for the new architecture: training a neural network having the new architecture until termination criteria for the training are satisfied; and determining a final measure of fitness of the neural network having the new architecture after the training; and adding data defining the new architecture and the final measure of fitness for the neural network having the new architecture to the population data.

Computationally efficient neural network architecture search

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing images using an image processing neural network system. One of the systems includes a domain transformation neural network implemented by one or more computers, wherein the domain transformation neural network is configured to: receive an input image from a source domain; and process a network input comprising the input image from the source domain to generate a transformed image that is a transformation of the input image from the source domain to a target domain that is different from the source domain.

Transforming source domain images into target domain images

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San Francisco, CA, United States of America

David Martin Dohan