Microsoft Technology Licensing, LLC

Pinterest, Inc.

Massachusetts Institute of Technology

Thomson Licensing

Nadia Fawaz

Dmitry Olegovich Kislyuk

Rohan Mahadev

David Ding-Jia Xue

Valerie Moy

Bhawna Juneja

Anh Tuong Ta

William Yau

Jordan DiSanto

Ding Zhang

David Voiss

Graham Roth

Christopher Lee Schaefbauer

Hang Zhang

Muriel Medard

Nina Taft

Jean C Bolot

Aman Grover

Saurabh Kataria

Girish Kathalagiri Somashekariah

Benjamin Hoan Le

Aleksandar Nikolov

Caleb Timothy Johnson

Guoyin Wang

Described are systems and methods to determine hair patterns presented in content items. The determined hair patterns may be associated with the content items to facilitate indexing, filtering, etc. of the content items based on the determined hair patterns. In exemplary implementations, a corpus of content items may be associated with an embedding vector that includes a binary representation of the content item. The embedding vectors associated with each content item can be provided as inputs to a trained machine learning model, which can process the embedding vectors to determine one or more hair patterns presented in each content item while eliminating the need for performing image pre-processing prior to determination of the hair pattern(s) presented in the content item.

Hair pattern determination and filtering

The disclosed embodiments provide a system for processing data. During operation, the system receives, over a set of event streams, a set of logging events for actions performed between members and jobs over multiple channels. Next, the system aggregates a subset of the logging events spanning a logging window by a reference identifier (ID) generated based on a user session of a member, a first member ID for the member, and a first job ID for a job. The system then creates, based on a unified data logic, a record containing a subset of the actions represented by the logging events and contexts for the subset of the actions. Finally, the system outputs the record for use in subsequent analysis associated with the member and the job.

Unified logging of actions for labeling

In an example, features in a boosting decision tree model are initialized to zero, the boosting decision tree model located in a GLMM and connected to a deep neural network collaborative filtering model via a prediction layer. While the features in the boosting decision tree model remain zero, the deep neural network collaborative filtering model is trained. One or more trees in the boosting decision tree model are boosted using logits produced by the training of the deep neural network collaborative filtering model as a margin. The prediction layer is trained using features from the deep neural network collaborative filtering model and features from the boosting decision tree model. It is then determined whether a set of convergence criteria is met. If not, then the deep neural network collaborative filtering model is retrained using the features and the process is repeated until the set of convergence criteria is met.

Deep and wide machine learned model for job recommendation

Described herein is a method and system for providing privacy guarantees with an improved privacy-accuracy trade-off. Dynamic data can be accessed from a database. A sum model is selected from window sum, exponential decay sum, and polynomial decay sum. An algorithm is initiated that produces polylogarithmic bounded error in the range of a sum function associated with the selected sum model and independent of time steps. The data can be assembled in a dyadic tree structure. A non-linearity component can be added to nodes of the dyadic tree structure. For example, this can be a noise components or a weight applied to the update. This can be done, for example, to different nodes differently. Differential private estimators can be constructed for fixed steps of time. The differential private estimators can be applied to a query means or filtering system to enhance privacy protection from potential adversaries.

Private decayed sum estimation under continual observation

A method, apparatus and computer program product for a peaky binning Relaying Scheme for Wideband/Low Signal-To-Noise Ratio (SNR) Wireless Communications is presented. In a computer system having a source node (S), a relay node (R) and a destination node (D), the method includes transmitting by S a message (m), wherein a first subset of m (m) is transmitted to R and wherein a second subset of m (m) is transmitted to D. The method further includes decoding by R a bin index from the mand forwarding the bin index to D. Additionally the method includes receiving the bin index at D and decoding the bin index and receiving at D the mand decoding the m. The method also includes determining by D the value of m from decoded values of the bin index and the m.

Peaky binning relaying scheme for wideband/low signal-to-noise ratio (SNR) wireless communications

Growing community of inventors

Santa Clara, CA, United States of America

Nadia Fawaz