Google Inc.

Hewlett-packard Development Company, L.p.

Interval Research Corporation

Interval Licensing LLC

Vulcan Patents LLC

Yesvideo, Inc.

Electric Planet Interactive

Michele Mae Covell

Shumeet Baluja

David Marwood

Michael Chu

Joshua J Sacks

Rohan Seth

Malcolm G Slaney

Vivek Kwatra

Mei Han

Michael Harville

Saurabh Mathur

Sergey Ioffe

Subutai Ahmad

Steven E Saunders

Sumit Roy

Nicholas M Johnston

George Dan Toderici

Neal A Bhadkamkar

David Charles Minnen

Sung Jin Hwang

Yushi Jing

Susie J Wee

M Margaret Withgott

Michael Fink

Bonnie M Johnson

Joel Eric Shor

Damien Vincent

John Ankcorn

Bo Shen

Harold G Sampson

Nina Bhatti

Rahul Sukthankar

Saurabh Singh

Paul A Freiberger

J Thomas Ngo

Trevor J Darrell

Annarosa Tomasi

Arthur Rothstein

Gavin Miller

Debargha Mukherjee

John George Apostolopoulos

Charles Joseph Rosenberg

Wai-Tian Tan

Zhengrong Ji

Gheorghe Postelnicu

Henry Harlyn Baker

John Iselin Woodfill

Gaile Gordon

Jingbin Wang

Sarah Parker

Chen Wang

Ramin D Zabih

Richard Tucker

Sean O'Malley

Harlyn Baker

Stephen Conor Holiday

Ali Rahimi

Katerina L Shiffer

Sarah Moussa

Emil Keyder

Michael Hongmai Lin

Jay Yagnik

Daniel M Keysers

Jens Eilstrup Rasmussen

Philippe P Piernot

Thomas Malzbender

Jeffrey L Edwards

Pascal Massimino

Mirjana Spasojevic

Jingyu Cui

Andrew R Golding

Magnus Karlsson

W Bruce Culbertson

Donald Ovila Tanguay, Jr

Jasmine Kent

Tiruvilwamalai Venkatraman Raman

Yuchen Wu

Leonidas Kontothanassis

Marcos R Vescovi

Charles Chapin

Frederic Huve

Christoph Bregler

Josh Sacks

Beomjoo Seo

Annarosa Tomasai

Jeff Faust

Gavin S P Miller

Subutai Ahmed

Henrik Rydgard

Jing-yu Cui

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

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

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for image compression and reconstruction. A request to generate an encoded representation of an input image is received. The encoded representation of the input image is then generated. The encoded representation includes a respective set of binary codes at each iteration. Generating the set of binary codes for the iteration from an initial set of binary includes: for any tiles that have already been masked off during any previous iteration, masking off the tile. For any tiles that have not yet been masked off during any of the previous iterations, a determination is made as to whether a reconstruction error of the tile when reconstructed from binary codes at the previous iterations satisfies an error threshold. When the reconstruction quality satisfies the error threshold, the tile is masked off.

Stop code tolerant image compression neural networks

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for image compression and reconstruction. An image encoder system receives a request to generate an encoded representation of an input image that has been partitioned into a plurality of tiles and generates the encoded representation of the input image. To generate the encoded representation, the system processes a context for each tile using a spatial context prediction neural network that has been trained to process context for an input tile and generate an output tile that is a prediction of the input tile. The system determines a residual image between the particular tile and the output tile generated by the spatial context prediction neural network by process the context for the particular tile and generates a set of binary codes for the particular tile by encoding the residual image using an encoder neural network.

Tiled image compression using neural networks

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for compressing and decompressing data. In one aspect, an encoder neural network processes data to generate an output including a representation of the data as an ordered collection of code symbols. The ordered collection of code symbols is entropy encoded using one or more code symbol probability distributions. A compressed representation of the data is determined based on the entropy encoded representation of the collection of code symbols and data indicating the code symbol probability distributions used to entropy encode the collection of code symbols. In another aspect, a compressed representation of the data is decoded to determine the collection of code symbols representing the data. A reconstruction of the data is determined by processing the collection of code symbols by a decoder neural network.

Data compression by local entropy encoding

An apparatus for encoding a current frame of a video. The apparatus includes a memory and a processor. The processor is configured to execute instructions stored in the memory to generate, for each reference frame of a subset of available reference frames, at least one respective candidate global motion model (GMM); partition the current frame into blocks; generate an aggregated residual frame for the current frame; and encode the respective residual blocks in a compressed bitstream. To generate the aggregated residual frame includes to select, for predicting each block of the blocks, a respective selected GMM, where the respective selected GMM corresponds to the one of the at least one respective candidate GMMs that minimizes a total error associated with the aggregated residual frame; and obtain respective residual blocks for the block.

Diversified motion using multiple global motion models

An encoder system can include a pixel grid generator to receive an image having a first dimension, generate a grid having a second dimension, add a plurality of points to positions on the grid, and map a plurality of pixels of the image to the plurality of points. The encoder system can include a color module to assign a color to each of the plurality of points using a color table, a triangulation module to generate a plurality of vertices based on the plurality of points and triangulate the grid using the vertices, and a compression module to compress the vertices as a set of compressed vertex positions and a set of vertex colors.

Image compression and decompression using triangulation

Methods, and systems, including computer programs encoded on computer storage media for compressing data items with variable compression rate. A system includes an encoder sub-network configured to receive a system input image and to generate an encoded representation of the system input image, the encoder sub-network including a first stack of neural network layers including one or more LSTM neural network layers and one or more non-LSTM neural network layers, the first stack configured to, at each of a plurality of time steps, receive an input image for the time step that is derived from the system input image and generate a corresponding first stack output, and a binarizing neural network layer configured to receive a first stack output as input and generate a corresponding binarized output.

Image compression with recurrent neural networks

Encoding and decoding occupancy information is disclosed. A method includes determining row sums for the region, determining column sums for the region, encoding, in a compressed bitstream, at least one of the row sums and the column sums, and encoding, in the compressed bitstream and based on a coding order, at least one of the rows and the columns of the region. The coding order is based on the encoded at least one of the row sums and the column sums. The row sums include, for each row of the region, a respective count of a number of locations in the row having a specified value. The column sums include, for each column of the region, a respective count of a number of locations in the column having the specified value. A location having the specified value is indicative of the occupancy information at the location.

Compression of occupancy or indicator grids

A method for encoding a current frame of a video includes jointly determining respective motion models for reference frames and encoding the current frame using the respective motion models. The reference frames are used for encoding the current frame. Jointly determining respective motion models for reference frames includes determining respective aggregated residuals for combinations of candidate motion models and selecting the combination of candidate motion models that corresponds to the smallest aggregated residual. The respective motion models correspond to the candidate motion models of the selected combination.

Methods, systems, and computer program products are provided for determining content items for delivery to users based on imagery. One example method includes determining a location of a user or a location associated with a query submitted by the user, identifying imagery associated with the determined location, and determining one or more content items for delivery to the user based at least in part on the imagery.

Determining content for delivery based on imagery

Growing community of inventors

Los Altos Hills, CA, United States of America

Michele Mae Covell