Experian Information Solutions, Inc.

Insurance Services Office, Inc.

Julian Yarkony

Yi He

Maneesh Kumar Singh

Hua Li

Shanji Xiong

Zhixuan Wang

Kevin Chen

Mason Carpenter

Christer DiChiara

Mark Hirn

Chi Zhang

Sophie Liu

Vishnu Sai Rao Suresh Lokhande

Konrad Kording

Shaofei Wang

The present disclosure recites systems and methods for generating record clusters. For example, a computer-implemented method comprises receiving a plurality of records from data sources and providing at least a subset of the records to a scoring model that determines scores for various pairings of the records, a score for a given pair of the records representing a probability that the given pair of records contain data elements about the same entity. The method further comprises generating a graph data structure that includes a plurality of nodes, individual nodes representing a different record from the records. The method also comprises assigning a different unique identifier to individual clusters of the final clusters and responding to a request for data regarding a given entity by providing aggregated data elements from those records of the records associated with a cluster of the final clusters having an identifier that represents the given entity.

Single identifier platform for storing entity data

Computer vision systems and methods for end-to end training of neural networks are provided. The system generates a fixed point algorithm for dual-decomposition of a maximum-a-posteriori inference problem and trains the convolutional neural network and a conditional random field with the fixed point algorithm and a plurality of images of a dataset to learn to perform semantic image segmentation. The system can segment an attribute of an image of the dataset by the trained neural network and the conditional random field.

Computer vision systems and methods for end-to-end training of convolutional neural networks using differentiable dual-decomposition techniques

Systems and methods are described for generating record clusters. The methods comprise receiving a plurality of records from data sources and providing at least a subset of the records to a scoring model that determines scores for various pairings of the records, a score for a given pair of the records representing a probability that the given pair of records contain data elements about the same entity. The method further comprises generating a graph data structure that includes a plurality of nodes, individual nodes representing a different record from the records. The method also comprises assigning a different unique identifier to individual clusters of the final clusters and responding to a request for data regarding a given entity by providing aggregated data elements from those records of the records associated with a cluster of the final clusters having an identifier that represents the given entity.

Computer vision systems and methods for optimizing correlation clustering for image segmentation are provided. The system receives input data and generates a correlation clustering formulation for Benders Decomposition for optimized correlation clustering of the input data. The system optimizes the Benders Decomposition for the generated correlation clustering formulation and performs image segmentation using the optimized Benders Decomposition.

Computer vision systems and methods for optimizing correlation clustering for image segmentation using Benders decomposition

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Jersey City, NJ, United States of America

Julian Yarkony