Paypal, Inc.

University of California

Shiwen Shen

Feng Q Pan

Danielle Zhu

Kyung Hyun Sung

William Hsu

Xinran Zhong

A unified model for a neural network can be used to predict a particular value, such as a customer value. In various instances, customer value may have particular sub-components. Taking advantage of this fact, a specific learning architecture can be used to predict not just customer value (e.g. a final objective) but also the sub-components of customer value. This allows improved accuracy and reduced error in various embodiments.

Unified artificial intelligence model for multiple customer value variable prediction

While artificial neural networks can be used to predict particular values in certain contexts, convolutional neural networks are not typically used in these contexts&#x2014;instead they may be employed for image recognition. However, raw transactional data may be structured to take advantage of convolutional neural network (CNN) techniques by arranging the data such that correlations are increased between nearby other data. In arranging data in this manner, the structured CNN (SCNN) can operate efficiently without having to make use of engineered data features, the generation and maintenance of which can be a time-consuming process.

Convolutional neural networks for variable prediction using raw data

An automatic classification method for distinguishing between indolent and clinically significant carcinoma using multiparametric MRI (mp-MRI) imaging is provided. By utilizing a convolutional neural network (CNN), which automatically extracts deep features, the hierarchical classification framework avoids deficiencies in current schemes in the art such as the need to provide handcrafted features predefined by a domain expert and the precise delineation of lesion boundaries by a human or computerized algorithm. This hierarchical classification framework is trained using previously acquired mp-MRI data with known cancer classification characteristics and the framework is applied to mp-MRI images of new patients to provide identification and computerized cancer classification results of a suspicious lesion.

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Shiwen Shen