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公开(公告)号：US20190332569A1
公开(公告)日：2019-10-31
申请号：US15964586
申请日：2018-04-27
发明人： Yiming Ma , Wei Lu , Jun Jia , Bee-Chung Chen , Bo Long
摘要： In an example embodiment, knowledge discovery using deep learning is combined with the scalability and personalization capabilities of generalized additive mixed effect (GAME) modeling. Specifically, features learned in a last fully connected layer of a deep learning model may be used to augment features used in a fixed or random effects training portion of a GAME model.
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公开(公告)号：US20190258984A1
公开(公告)日：2019-08-22
申请号：US15898847
申请日：2018-02-19
发明人： Muhammad Ebadur Rehman , Huiji Gao , Jun Jia , Bo Long
摘要： Techniques for predicting sequential data using generative adversarial networks are disclosed herein. In some embodiments, a method comprises: receiving a request associated with a user of an online service; in response to the receiving of the request, retrieving a first plurality of sequential data points of the user from a profile of the user stored on a database of the online service, the first plurality of sequential data points comprising at least one attribute for each one of a plurality of sequential career points of the user; generating at least one predicted data point for the user based on the first plurality of sequential data points using a generative model, the generated at least one predicted data point comprising at least one attribute for a predicted career point for the user; and performing a function of the online service using the generated at least one predicted data point.
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公开(公告)号：US11704566B2
公开(公告)日：2023-07-18
申请号：US16446924
申请日：2019-06-20
发明人： Yiming Ma , Menglin L. Brown , Bee-Chung Chen , Sheng Wu , Jun Jia , Bo Long
IPC分类号： G06N3/00 , G06N3/082 , G06N20/20 , G06F11/34 , G06F18/214
CPC分类号： G06N3/082 , G06F11/3495 , G06F18/214 , G06N20/20
摘要： The disclosed embodiments provide a system for processing data. During operation, the system obtains a training dataset containing a first set of records associated with a first set of identifier (ID) values and an evaluation dataset containing a second set of records associated with a second set of ID values. Next, the system selects a random subset of ID values from the second set of ID values. The system then generates a sampled evaluation dataset comprising a first subset of records associated with the random subset of ID values in the second set of records. The system also generates a sampled training dataset comprising a second subset of records associated with the random subset of ID values in the first set of records. Finally, the system outputs the sampled training dataset and the sampled evaluation dataset for use in training and evaluating a machine learning model.
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公开(公告)号：US20200311613A1
公开(公告)日：2020-10-01
申请号：US16370156
申请日：2019-03-29
发明人： Yiming Ma , Jun Jia , Yi Wu , Xuhong Zhang , Leon Gao , Baolei Li , Bee-Chung Chen , Bo Long
摘要： Herein are techniques for configuring, integrating, and operating trainable tensor transformers that each encapsulate an ensemble of trainable machine learning (ML) models. In an embodiment, a computer-implemented trainable tensor transformer uses underlying ML models and additional mechanisms to assemble and convert data tensors as needed to generate output records based on input records and inferencing. The transformer processes each input record as follows. Input tensors of the input record are converted into converted tensors. Each converted tensor represents a respective feature of many features that are capable of being processed by the underlying trainable models. The trainable models are applied to respective subsets of converted tensors to generate an inference for the input record. The inference is converted into a prediction tensor. The prediction tensor and input tensors are stored as output tensors of a respective output record for the input record.
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