Verizon Patent and Licensing Inc.

Yahoo! Inc.

Aol Inc.

Verizon Media Inc.

Nvidia Corporation

Oath Inc.

Google Inc.

Yahoo Ad Tech LLC

Andrew Feng

Erik Ordentlich

Peter Cnudde

Jun Shi

Bruno M Fernandez-Ruiz

Lee Yang

N Nachiappan

Lin Shen

Rohit Chandra

Mridul Jain

Daguang Xu

Valerie Kucharewski

Yan Cheng

Fausto Milletari

Wenqi Li

Joel Pulliam

Atul Bhandari

Milind Rao

Rong Ou

Nicola Christin Rieke

Utkarsh Srivastava

Swee Boon Lim

Brian Frank Cooper

Philip Lewis Bohannon

Daniel E Weaver

Raymond Paul Stata

Ramana Yerneni

Evgeniy Makeev

Scott Banachowski

Raghu Ramakrishnan

Michael David Bigby

Wentao Zhu

Jeffrey Budzinski

Bryan Call

Maximilian Baust

Charles Jonathan Hancox

Lakshmanan Suryanarayanan

Timothy R Crowder

Victor J Lam

David Lomax

The present teaching relates to distributed deep machine learning on a cluster. In one example, a request is received for estimating one or more parameters associated with a machine learning model on a cluster including a plurality of nodes. A set of data is obtained to be used for estimating the one or more parameters. The set of data is divided into a plurality of sub-sets of data, each of which corresponds to one of the plurality of nodes. Each sub-set of data is allocated to a corresponding node for estimating values of the one or more parameters based on the sub-set of data. Estimated values of the one or more parameters obtained based on a corresponding sub-set of data allocated to the node, are received from each of the plurality of nodes. The one or more parameters of the machine learning model are estimated based on the estimated values of the one or more parameters generated by at least some of the plurality of nodes.

Method and system for distributed deep machine learning

A unified end-user notification platform delivers event alerts to different types of clients including mobile devices and HTTP clients. Users can subscribe to a plurality of notification channels and select from the associated various delivery options via a single user interface. The events are received by the unified notification platform which matches the received events with the user subscription data to identify subscribers and their respective delivery options. Corresponding event alerts are generated and delivered based on the user or subscriber specified options. Multiple event alerts corresponding to public and private data notification channels are provided to a user device via a single connection.

Unified end user notification platform

Apparatuses, systems, and techniques to perform federated training of neural networks while maintaining control over dissemination of local models of neural networks from which aspects of local training data might be extracted. In at least one embodiment, a neural network is trained on local training data and a local model is provided to be aggregated with other local models into a global model that is in turn used for further local model training, wherein a provided local model or training is adjusted to reduce an ability to extract aspects of local training data therefrom.

Neural network trained using federated learning with local training data preserved at local edge circuits

The present teaching relates to obtaining a model for identifying content matching a query. Training data are received which include queries, advertisements, and hyperlinks. A plurality of subwords are identified from each of the queries and a plurality of vectors for the plurality of subwords of each of the queries are obtained. Via a neural network, a vector for each of the queries is derived based on a plurality of vectors for the plurality of subwords of the query. A query/ads model is obtained via optimization with respect to an objective function, based on vectors associated with the plurality of subwords of each of the queries and vectors for the queries obtained from the neural network.

System and method for query to ad matching using deep neural net based query embedding

Apparatuses, systems, and techniques to collaboratively train one or more machine learning models. Parameter reviewers may be configured to compare sets of machine learning model parameter information in order to generate one or more machine learning models, such as neural networks.

Processor and system to train machine learning models based on comparing accuracy of model parameters

The present teaching relates to estimating one or more parameters on a system including a plurality of nodes. In one example, the system comprises: one or more learner nodes, each of which is configured for generating information related to a group of words for estimating the one or more parameters associated with a machine learning model; and a plurality of server nodes, each of which is configured for obtaining a plurality of sub-vectors each of which is a portion of a vector that represents a word in the group of words, updating the sub-vectors based at least partially on the information to generate a plurality of updated sub-vectors, and estimating at least one of the one or more parameters associated with the machine learning model based on the plurality of updated sub-vectors.

Method and system for distributed machine learning

The present teaching relates to obtaining a model for identifying content matching a query. Training data are received which include queries, advertisements, and hyperlinks. A plurality of subwords are identified from each of the queries and a plurality of input vectors for the plurality of subwords of each of the queries are obtained and an input vector for each of the queries is derived based on a plurality of input vectors of a plurality of subwords of the query. A query/ads model is optimized with respect to an objective function via training an input vector for each of the plurality of subwords associated with each of the queries, an input vector for each of the advertisements and links, and a matrix.

System and method for query to ad matching via optimizing input vectors of query subwords

The invention provides a Web service which enables a publisher to share his digital resources such as an address card or a calendar with a number of subscribers based on different sharing relationships. The Web service includes a host-based interface called 'My Community', for example, with which the publisher manages the share-relationships with his community members. The community members are organized into different groups. Each group includes a number of community members who have a common sharing relationship with the publisher with respect to one or more views of the shared resources. A resource may have multiple views. Each of the views has Metadata describing sharing-styles, as well as version, creation date, size, and the like. Each sharing style corresponds to a specific sharing relationship between a community member and the publisher.

System and method for community centric resource sharing based on a publishing subscription model

Methods and apparatus for managing file distribution between publishing and subscribing devices are provided. At a transporter system, a publish request for publishing a version of the dataset is received from a publisher device, wherein the publish request has a predefined format that includes an identity of the dataset, an identity of the version of the dataset, and an identity of a location for the version of the dataset. The transporter system updates a registry to associate the dataset with the identity of the dataset, version, and location, and the transporter system automatically publishes the dataset to any subscribers devices that have previously sent subscribe requests for such dataset to the transporter system.

Publish-subscribe platform for cloud file distribution

Methods and apparatus for executing an application are disclosed. In accordance with one embodiment, a request is received. One or more of a plurality of module types are instantiated such that a plurality of module objects are generated. A query plan linking the plurality of module objects is executed such that a response to the request is generated. The response is then returned.

Composition model for cloud-hosted serving applications

Growing community of inventors

Cupertino, CA, United States of America

Andrew Feng