Google Inc.

Abhanshu Sharma

Matthew Nirvan Sharifi

David Petrou

Wei Huang

Alexa Greenberg

Muhammad Mohsin

Jacek Slawosz Jurewicz

Jing Cao

Yanchao Su

Nicholas Kong

Benjamin Sidhom

Marcin M Nowak-Przygodzki

Gabriel Taubman

Ilya Firman

Adam Coimbra

James Stout

Joseph Lange

Thomas Weedon Hume

Sammy El Ghazzal

Gökhan Bakir

Alexander Mordvintsev

Jindong Chen

Thomas Binder

Jacek Szmigiel

Kyle Pedersen

Lukas Mach

Fedir Zubach

Reed Enger

Vishwath Mohan

Yunfan Jin

Gilles Baechler

A computing device is disclosed that includes at least one processor and a machine-learned model. The machine-learned model can include a plurality of blocks and one or more residual connections between two or more of the plurality of blocks. The machine-learned model can be configured to receive a model input and, in response to receipt of the model input, output a model output. The machine-learned model can be configured to perform operations including determining a resource allocation parameter that corresponds to a desired allocation of system resources to the machine-learned model at an inference time: deactivating a subset of the plurality of blocks of the machine-learned model based on the resource allocation parameter: inputting the model input into the machine-learned model with the subset of the plurality of blocks deactivated; and receiving, as an output of the machine-learned model, the model output.

Systems and methods for providing a machine-learned model with adjustable computational demand

Methods, systems, and apparatus for receiving a query image, receiving one or more entities that are associated with the query image, identifying, for one or more of the entities, one or more candidate search queries that are pre-associated with the one or more entities, generating a respective relevance score for each of the candidate search queries, selecting, as a representative search query for the query image, a particular candidate search query based at least on the generated respective relevance scores and providing the representative search query for output in response to receiving the query image.

Mapping images to search queries

Methods, systems, and apparatus for receiving a query image and a user tap location, processing the received query image based on the user tap location, identifying one or more entities associated with the processed query image and in response to receiving (i) the query image, and (ii) the user tap location, providing information about the identified one or more of the entities.

Visual recognition using user tap locations

Implementations set forth herein relate to an automated assistant that can interact with applications that may not have been pre-configured for interfacing with the automated assistant. The automated assistant can identify content of an application interface of the application to determine synonymous terms that a user may speak when commanding the automated assistant to perform certain tasks. Speech processing operations employed by the automated assistant can be biased towards these synonymous terms when the user is accessing an application interface of the application. In some implementations, the synonymous terms can be identified in a responsive language of the automated assistant when the content of the application interface is being rendered in a different language. This can allow the automated assistant to operate as an interface between the user and certain applications that may not be rendering content in a native language of the user.

Automated assistant control of non-assistant applications via identification of synonymous term and/or speech processing biasing

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a second model to approximate the output of a first model to classify, according to a classification scheme, image data received as input, and after the second model is trained accessing map data that specifies a plurality of geographic locations, and for each geographic location associated with an entity for each image of the one or more images that depict the entity located at the geographic location, providing the image to the second model to generate an embedding for the image, associating each of the one or more embeddings generated by the second model with the geographic location, and storing, in a database, location data specifying the geographic location, the associated one or more embeddings, and data specifying the entity, as an associated entity entry for the entity.

On-device image recognition

A computing device is described that includes at least one processor and a memory including instructions that when executed cause the at least one processor to output, for display, a graphical keyboard comprising a plurality of keys, and determine, based on an indication of a selection of one or more keys from the plurality of keys, text of an electronic communication. The instructions, when executed, further cause the at least one processor to identify, based at least in part on the text, a searchable entity or trigger phrase, generate, based on the searchable entity or trigger phrase, a search query, and output, for display, within the graphical keyboard, a graphical indication to indicate that the computing device generated the search query.

Search query predictions by a keyboard

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Zurich, Switzerland

Abhanshu Sharma