Google Inc.

Sap Se

Cadence Design Systems, Inc.

Jing Wang

Yang Jiao

Yang Zhang

Zhuo Zhang

Ruoyi Jiang

Dinah Shender

Sen Li

Junji Takagi

Mert Dikmen

Amy Richardson

Xinlong Bao

Vinod Kumar Ramachandran

Jian Liu

An-Yu Kuo

Fei Li

Chun-Teh Kao

Richa Namballa

Ali Nasiri Amini

Mario Ponce Midence

James Rapp

Pankti Jayesh Kansara

Ninad Kulkarni

Zhe Liu

Steve Swan

Peng Tang

Alex Valle

Victor Kasatkin

Methods, systems, and computer programs encoded on a computer storage medium, for training and using machine learning models are disclosed. Methods include creating a model that represents relationships between user attributes, content exposures, and performance levels for a target action using organic exposure data specifying one or more organic exposures experienced by a particular user over a specified time prior to performance of a target action by the particular user and third party exposure data specifying third party exposures of a specified type of digital component to the particular user over the specified time period. Using the model, an incremental performance level attributable to each of the third party exposures at an action time when the target action was performed by the particular user is determined. Transmission criteria for at least some digital components to which the particular user was exposed are modified based on the incremental performance.

Contribution incrementality machine learning models

The present disclosure involves systems, software, and computer implemented methods for automatically identifying table locations and table cell types of located tables. One example method includes receiving a request to detect tables. Features are extracted from an input spreadsheet and provided to a trained table detection model trained to predict whether worksheet cells are table cells or background cells and to a cell classification model that is trained to classify worksheet cells by cell structure type. The table detection model generates binary classifications that indicate whether cells are table cells or background cells. A contour detection process is performed on the binary classifications to generate table location information that describes at least one table boundary in the spreadsheet. The trained cell classification model generates a cell structure type classification for each cell that is included in a table boundary generated by the contour detection process.

Automatically determining table locations and table cell types

The present disclosure involves systems, software, and computer implemented methods for proactively predicting demand based on sparse transaction data. One example method includes receiving a request to predict transaction dates for a plurality of transaction entities for a future time period. Historical transaction data for the transaction entities is identified for a plurality of categories of transacted items. The plurality of categories are organized using a hierarchy of levels. Multiple levels of the hierarchy are iterated over, starting at a lowest level. For each current level in the iteration, a plurality of transaction date prediction models are trained and tested. Heuristics for the plurality of trained transaction date prediction models are compared to determine a most accurate transaction date prediction model. The most accurate transaction date prediction model is used to make a prediction of transaction dates for the current level for the future time period.

Proactively predicting transaction dates based on sparse transaction data

Disclosed are methods, systems, and articles of manufacture for implementing an electronic design having embedded circuits. These techniques identify a specification of an electronic design, a parameter for optimization, at least one optimization target for the parameter, and initial grids for the electronic design. An optimization map may be determined, by at one or more optimization modules that are stored at least partially in memory of and function in conjunction with at least one microprocessor of a computing system, for the electronic design at least by performing one or more analyses that refine the initial grids for the optimization map with respect to the parameter and the at least one optimization target. The electronic design may be implemented based at least in part upon the optimization map.

Methods, systems, and computer program product for implementing an electronic design with optimization maps

The present disclosure is directed to a technique to reduce latency in feedback-based system performance determination. A system receives, from an application developer device, indications of an in-application event and a first input value for an application content delivery profile. The system receives, via an interface from an application developed by an application developer and executed by a computing device remote from the data processing system and different from the application developer device, a ping indicative of an occurrence of the in-application event on the computing device. The system merges data from the ping with internal data determined by the data processing system to generate merged data. The system determines a predicted performance for the in-application event and provides an indication of the predicted performance. The system configures, responsive to the indication of the predicted performance, the application content delivery profile with a second input value.

Latency reduction in feedback-based system performance determination

Growing community of inventors

Santa Clara, CA, United States of America

Jing Wang