Google Inc.

Shumeet Baluja

Michele Mae Covell

Yushi Jing

Michael Chu

Rohan Seth

David Marwood

Henry Allan Rowley

Elad Gil

Maryam Kamvar

Joshua J Sacks

Cedric Beust

Richard Carl Gossweiler, Iii

Jay Yagnik

Maryam Garrett

Dandapani Sivakumar

Michael H Cohen

Vivek Kwatra

Mei Han

Mehran Sahami

Vibhu Mittal

Saurabh Mathur

Mayumi Matsuno

Michael Fink

Sergey Ioffe

Nicholas M Johnston

Pedro J Moreno

Pedro Jose Moreno Mengibar

Zhengrong Ji

Michael Alex Frumkin

Sanjay Gajanan Mavinkurve

Rahul Sukthankar

Andrew R Golding

Arungundram C Narendran

Deepak Ravichandran

Doug Beeferman

Pascal Massimino

Fritz Schneider

Niels Provos

Chris Barton

Thomas Leung

Yuchen Wu

Mark Crady

Pedro J Mengibar

Eric L Davis

Josh Sacks

Avichal Garg

Jeff Faust

Richard Carl Iii Gossweiler

Techniques for identifying groups of local features in an image and presenting advertisement information associated with stored images that match one or more features within the group of local features are described. The techniques include providing an image, identifying a region of interest in the image, providing a user-selectable link associated with the region of interest in the image, receiving a request for the region of interest in the image via the user-selectable link, and presenting advertisement information associated with a stored image that matches one or more features within the requested region of interest.

Informational and advertiser links for use in web mapping services

The present disclosure provides systems and methods that train and use neural networks that can be run with no multiplications and no floating point operations. In particular, according to one aspect of the present disclosure, the respective non-linear and continuous activation functions typically used by the nodes of a neural network can be replaced with custom activation functions that output one of a discrete number of activation values. Likewise, according to another aspect of the present disclosure, the neural network can be trained such that each of its weights equals one of a discrete number of weight values. Taken together, this enables replacement of the typical multiplication process associated with computing a node of the network with a simple, and much faster, lookup process. In particular, a lookup table can store the result of multiplying each unique pair of activation value and weight value.

Deep neural networks with no multiplications and no floating point operations

The present disclosure is directed to encoding images. In particular, one or more computing devices can receive data representing one or more machine learning (ML) models configured, at least in part, to encode images comprising objects of a particular type. The computing device(s) can receive data representing an image comprising one or more objects of the particular type. The computing device(s) can generate, based at least in part on the data representing the image and the data representing the ML model(s), data representing an encoded version of the image that alters at least a portion of the image comprising the object(s) such that when the encoded version of the image is decoded, the object(s) are unrecognizable as being of the particular type by one or more object-recognition ML models based at least in part upon which the ML model(s) configured to encode the images were trained.

Methods and systems for encoding images

In accordance with some embodiments of the disclosed subject matter, mechanisms for seamless audio melding between audio items in a playlist are provided. In some embodiments, a method for transitioning between audio items in playlists is provided, comprising: identifying a sequence of audio items in a playlist of audio items, wherein the sequence of audio items includes a first audio item and a second audio item that is to be played subsequent to the first audio item; and modifying an end portion of the first audio item and a beginning portion of the second audio item, where the end portion of the first audio item and the beginning portion of the second audio item are to be played concurrently to transition between the first audio item and the second audio item, wherein the end portion of the first audio item and the beginning portion of the second audio item have an overlap duration, and wherein modifying the end portion of the first audio item and the beginning portion of the second audio item comprises: generating a first spectrogram corresponding to the end portion of the first audio item and a second spectrogram corresponding to the beginning portion of the second audio item; identifying, for each frequency band in a series of frequency bands, a window over which the first spectrogram within the end portion of the first audio item and the second spectrogram within the beginning portion of the second audio item have a particular cross-correlation; modifying, for each frequency band in the series of frequency bands, the end portion of the first spectrogram and the beginning portion of the second spectrogram such that amplitudes of frequencies within the frequency band decrease within the first spectrogram over the end portion of the first spectrogram and that amplitudes of frequencies within the frequency band increase within the second spectrogram over the beginning portion of the second spectrogram; and generating a modified version of the first audio item the includes the modified end portion of the first audio item based on the modified end portion of the first spectrogram and generating a modified version of the second audio item that includes the modified beginning portion of the second audio item based on the modified beginning portion of the second spectrogram.

Methods, systems, and media for seamless audio melding between songs in a playlist

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing a network input using a neural network to generate a network output for the network input. One of the methods includes maintaining, for each of the plurality of neural network layers, a respective look-up table that maps each possible combination of a quantized input index and a quantized weight index to a multiplication result; and generating a network output from a network input, comprising, for each of the neural network layers: receiving data specifying a quantized input to the neural network layer, the quantized input comprising a plurality of quantized input values; and generating a layer output for the neural network layer from the quantized input to the neural network layer using the respective look-up table for the neural network layer.

Look-up table based neural networks

A computer-implemented method for generating results for a client-requested query involves receiving a query produced by a client communication device, generating a result for the query in response to reception of the query, determining one or more predictive follow-up requests before receiving an actual follow-up request from the client device, and initiating retrieval of information associated with the one or more predictive follow-up requests, and transmitting at least part of the result to the client device, and then transmitting to the client device at least part of the information associated with the one or more predictive follow-up requests.

Predictive information retrieval

Methods, systems and apparatus, including computer programs encoded on a computer storage medium for selecting keywords for resources are disclosed. In one aspect, a search query is received associated with a first user. A determination is made that the first user is a follower of an entity feed that is provided by a first entity and that is provided through a social network. A content item is selected having distribution parameters specifying that the content item is to be provided to users that are followers of the entity feed and that submit the search query. The selected content item is provided for the first user.

Providing content in response to user actions

Systems and techniques, including computer software, for retrieving information to a mobile device involve installing a data retrieval application on the mobile device. The data retrieval application includes instructions for presenting a structured data display on the mobile device, defining a structure of the structured data display, requesting selected hyperlinks included in the structured data display, and rendering markup language information received in response to the selected hyperlinks. A user request to retrieve data through the data retrieval application is received, and data is retrieved in response to the received user request. The retrieved data is displayed according to the structure of the structured data display, and a user can select a hyperlink in the displayed data to retrieve and render markup language information using the data retrieval application.

Customized data retrieval applications for mobile devices providing interpretation of markup language data

Ad rendering parameters for a set of two or more ads may be determined by (a) accepting, for a set of two or more ads, ad information which includes at least one ad feature having a value that depends on ad rendering parameters, and (b) determining ad rendering parameters for at least one ad from the set of two or more ads using the accepted ad information. The act of determining ad rendering parameters may use accepted ad rendering constraints. The ad rendering constraints may include space available for rendering the ads, a footprint available for rendering the ads, and/or a maximum number of ads permitted to be rendered. The act of determining ad rendering parameters may include maximizing a value associated with serving at least one ad from the set of two or more ads with ad rendering parameters subject to the ad rendering constraints. The ad rendering parameters may include sizes of the served ads, and/or a layout of the served ads.

Ad rendering parameters, such as size, style, and/or layout, of online ads

A computer-implemented method of providing personalized route information involves gathering a plurality of past location indicators over time for a wireless client device, determining a future driving objective using the plurality of previously-gathered location indicators, obtaining real-time traffic data for an area proximate to the determined driving objective, and generating a suggested route for the driving objective using the near real-time traffic data.

Transportation routing

The present disclosure provides systems and methods for hiding information using deep neural networks. In one example, a computer-implemented method is provided to train neural networks for hiding images, which includes inputting a package image and a cover image into an image hiding neural network and generating a carrier image as an output, the carrier image comprising the package image hidden within the cover image. The method includes inputting the carrier image into an image decoding neural network and generating a reconstruction of the package image as an output. The method includes simultaneously training the image decoding neural network based at least in part on a first loss function that describes a difference between the package image and the reconstruction of the package image and the image hiding neural network based at least in part on the first loss function and on a second loss function that describes a difference between the cover image and the carrier image.

Hiding information and images via deep learning

Growing community of inventors

Leesburg, VA, United States of America

Shumeet Baluja