Google Inc.

Augustus Quadrozzi Odena

Quoc V Le

Dengyong Zhou

Ian Goodfellow

David J Bieber

Ed Huai-Hsin Chi

Aakanksha Chowdhery

David Martin Dohan

Jacob Austin

Maarten Paul Bosma

John Dieterich Lawson

Samaneh Azadi

Dale Eric Schuurmans

Jason Weng Wei

Le Hou

Catherine Olsson

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

Olivier Jean Andrè Bousquet

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

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for adjusting neural network resource usage. One of the methods includes receiving a network input for processing by a task neural network, the task neural network comprising a plurality of neural network layers; receiving a usage input specifying a respective weight for each of one or more usage factors, wherein each usage factor impacts how many computational resources are used by the task neural network during the processing of the network input; and processing the network input using the task neural network in accordance with the usage input to generate a network output for the network input, comprising: selecting, based at least on the usage input, a proper subset of the plurality of neural network layers to be active while processing the network input, and processing the network input using only the selected neural network layers.

Adjusting neural network resource usage

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for providing a data sample in response to a request for a data sample. In one aspect, a method comprises: receiving a request for a new data sample; until a candidate new data sample is generated that satisfies an acceptance criterion, performing operations comprising: generating a candidate new data sample using a generator neural network; processing the candidate new data sample using a discriminator neural network to generate an imitation score; and determining, from the imitation score, whether the candidate new data sample satisfies the acceptance criterion; and providing the candidate new data sample that satisfies the acceptance criterion in response to the received request.

Sampling from a generator neural network using a discriminator neural network

The present disclosure provides systems and methods for debugging neural networks. In one example, a computer-implemented method is provided, which includes obtaining, by one or more computing devices, one or more inputs from an input corpus. The method further includes mutating, by the one or more computing devices, the one or more inputs and providing the one or more mutated inputs to a neural network; obtaining, by the one or more computing devices as a result of the neural network processing the one or more mutated inputs, a set of coverage arrays; determining, by the one or more computing devices based at least in part on the set of coverage arrays, whether the one or more mutated inputs provide new coverage; and upon determining that the one or more mutated inputs provide new coverage, adding the one or more mutated inputs to the input corpus.

Systems and methods for debugging neural networks with coverage guided fuzzing

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San Francisco, CA, United States of America

Augustus Quadrozzi Odena