Google Inc.

Samy Bengio

Charles Joseph Rosenberg

Gal Chechik

Jeffrey Adgate Dean

Gregory Sean Corrado

Yangli Hector Yee

Oriol Vinyals

Kai Chen

Sergey Ioffe

Matthieu Devin

Thomas J Duerig

Rajat Monga

Jason Edward Weston

Erik Murphy-Chutorian

Jonathon Shlens

Jay Yagnik

Alexander Toshev

Yoram Singer

Dumitru Erhan

Noam M Shazeer

Navdeep Jaitly

Andrea Lynn Frome

Kunlong Gu

Neil G Alldrin

Varun Sharma

Zhen Hao Zhou

David Petrou

Quoc V Le

Adam L Berenzweig

Jay Michael Ponte

Ron J Weiss

Ryan Michael Rifkin

Douglas Eck

Thomas Chadwick Walters

Arcot J Preetham

Tomas Mikolov

Sally A Goldman

Geremy A Heitz, Iii

Bin Shen

Richard Francis Lyon

Ignacio Lopez Moreno

Troy Chinen

Georg Heigold

Mohammad Norouzi

Yun Zhou

Maya Rani Gupta

Maks Ovsjanikovs

Olivier Bousquet

Martin Rehn

Thomas Walters

Alexander Toshkov Toshev

A system, computer readable storage medium, and computer-implemented method presents video search results responsive to a user keyword query. The video hosting system uses a machine learning process to learn a feature-keyword model associating features of media content from a labeled training dataset with keywords descriptive of their content. The system uses the learned model to provide video search results relevant to a keyword query based on features found in the videos. Furthermore, the system determines and presents one or more thumbnail images representative of the video using the learned model.

Relevance-based image selection

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating descriptions of input images. One of the methods includes obtaining an input image; processing the input image using a first neural network to generate an alternative representation for the input image; and processing the alternative representation for the input image using a second neural network to generate a sequence of a plurality of words in a target natural language that describes the input image.

Generating natural language descriptions of images

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for classifying data objects. One of the methods includes obtaining data that associates each term in a vocabulary of terms with a respective high-dimensional representation of the term; obtaining classification data for a data object, wherein the classification data includes a respective score for each of a plurality of categories, and wherein each of the categories is associated with a respective category label; computing an aggregate high-dimensional representation for the data object from high-dimensional representations for the category labels associated with the categories and the respective scores; identifying a first term in the vocabulary of terms having a high-dimensional representation that is closest to the aggregate high-dimensional representation; and selecting the first term as a category label for the data object.

Classifying data objects

This document generally describes a neural network training system, including one or more computers, that trains a recurrent neural network (RNN) to receive an input, e.g., an input sequence, and to generate a sequence of outputs from the input sequence. In some implementations, training can include, for each position after an initial position in a training target sequence, selecting a preceding output of the RNN to provide as input to the RNN at the position, including determining whether to select as the preceding output (i) a true output in a preceding position in the output order or (ii) a value derived from an output of the RNN for the preceding position in an output order generated in accordance with current values of the parameters of the recurrent neural network.

Training recurrent neural networks to generate sequences

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a model using parameter server shards. One of the methods includes receiving, at a parameter server shard configured to maintain values of a disjoint partition of the parameters of the model, a succession of respective requests for parameter values from each of a plurality of replicas of the model; in response to each request, downloading a current value of each requested parameter to the replica from which the request was received; receiving a succession of uploads, each upload including respective delta values for each of the parameters in the partition maintained by the shard; and updating values of the parameters in the partition maintained by the parameter server shard repeatedly based on the uploads of delta values to generate current parameter values.

Training a model using parameter server shards

Generating a playlist may include designating a seed track in an audio library; identifying audio tracks in the audio library having constructs that are within a range of a corresponding construct of the seed track, where the constructs for the audio tracks are derived from frequency representations of the audio tracks, and the corresponding construct for the seed track is derived from a frequency representation of the seed track; and generating the playlist using at least some of the audio tracks that were identified.

Generating a playlist

Systems and techniques are disclosed for labeling objects within an image. The objects may be labeled by selecting an option from a plurality of options such that each option is a potential label for the object. An option may have an option score associated with. Additionally, a relation score may be calculated for a first option and a second option corresponding to a second object in an image. The relation score may be based on a frequency, probability, or observance corresponding to the co-occurrence of text associated with the first option and the second option in a text corpus such as the World Wide Web. An option may be selected as a label for an object based on a global score calculated based at least on an option score and relation score associated with the option.

Label consistency for image analysis

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Los Altos, CA, United States of America

Samy Bengio