Google Inc.

David J Bieber

Rishabh Singh

Kensen Shi

Quoc V Le

Jonathon Shlens

Mohammad Norouzi

Dengyong Zhou

Kevin Patrick Murphy

Ed Huai-Hsin Chi

Augustus Quadrozzi Odena

Aakanksha Chowdhery

David Martin Dohan

Sergio Guadarrama Cotado

Jacob Austin

Maarten Paul Bosma

Dale Eric Schuurmans

Jason Weng Wei

Ryan Lienhart Dahl

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

The present disclosure provides systems and methods for synthesizing computer-readable code based on the receipt of input and output examples. A computing system in accordance with the disclosure can be configured to receive a given input and output, access and library of operations, and perform a search of a library of operations (e.g., transpose, slice, norm, etc.) that can be applied to the input. By applying the operations to the input and tracking the results, the computing system may identify an expression comprising one or a combination of operations that when applied to the input generates the output. In this manner, implementations of the disclosure may be used to identify one or more solutions that a user having access to the library of operations may use to generate the output from the input.

Systems and methods for synthesizing code from input and output examples

Systems and methods for transforming grayscale images into color images using deep neural networks are described. One of the systems include one or more computers and one or more storage devices storing instructions that, when executed by one or more computers, cause the one or more computers to implement a coloring neural network, a refinement neural network, and a subsystem. The coloring neural network is configured to receive a first grayscale image having a first resolution and to process the first grayscale image to generate a first color image having a second resolution lower than the first resolution. The subsystem processes the first color image to generate a set of intermediate image outputs. The refinement neural network is configured to receive the set intermediate image outputs, and to process the set of intermediate image outputs to generate a second color image having a third resolution higher than the second resolution.

Transforming grayscale images into color images using deep neural networks

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New York, NY, United States of America

David J Bieber