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公开(公告)号：US11204968B2
公开(公告)日：2021-12-21
申请号：US16449110
申请日：2019-06-21
发明人： Dan Liu , Daniel Sairom Krishnan Hewlett , Qi Guo , Wei Lu , Xuhong Zhang , Wensheng Sun , Mingzhou Zhou , Anthony Hsu , Keqiu Hu , Yi Wu , Chenya Zhang , Baolei Li
IPC分类号： G06F16/9038 , G06N3/02 , H04L29/08
摘要： In an example embodiment, a platform is provided that utilizes information available to a computer system to feed a neural network. The neural network is trained to determine both the probability that a searcher would select a given potential search result if it was presented to him or her and the probability that a subject of the potential search result would respond to a communication from the searcher. These probabilities are essentially combined to produce a single score that can be used to determine whether to present the searcher with the potential search result and, if so, how high to rank the potential search result among other search results. In a further example embodiment, embeddings used for the input features are modified during training to maximize an objective.
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公开(公告)号：US20200380407A1
公开(公告)日：2020-12-03
申请号：US16430243
申请日：2019-06-03
发明人： Chengming Jiang , Kinjal Basu , Wei Lu , Souvik Ghosh , Mansi Gupta
摘要： In an example embodiment, training data is obtained, the training data comprising values for a plurality of different features. Then a global machine learned model is trained using a first machine learning algorithm by feeding the training data into the first machine learning algorithm during a fixed effect training process. A non-linear first random effects machine learned model is trained by feeding a subset of the training data into a second machine learning algorithm, the subset of the training data being limited to training data corresponding to a particular value of one of the plurality of different features.
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公开(公告)号：US20200293536A1
公开(公告)日：2020-09-17
申请号：US16816882
申请日：2020-03-12
发明人： Wei Lu , Michael Kinoti , Shengquan Yan , Peng Yu , Xian Zhang , Guixi Zou , Yin He , Xavier Drudis Rius , Miriam Rosenberg , Zijian Zheng
IPC分类号： G06F16/2455
摘要： Architecture that decomposes of one or more monolithic data concepts into atomic concepts and related atomic concept dependencies, and provides streaming data processing that processes individual or separate (atomic) data concepts and defined atomic dependencies. The architecture can comprise data-driven data processing that enables the plug-in of new data concepts with minimal effort. Efficient processing of the data concepts is enabled by streaming only required data concepts and corresponding dependencies and enablement of the seamless configuration of data processing between stream processing systems and batch processing systems as a result of data concept decomposition. Incremental and non-incremental metric processing enables realtime access and monitoring of operational parameters and queries.
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公开(公告)号：US10678997B2
公开(公告)日：2020-06-09
申请号：US15825657
申请日：2017-11-29
发明人： Karan Ashok Ahuja , Befekadu Ayenew Ejigou , Ningfeng Liang , Lokesh P. Bajaj , Wei Wang , Paul Fletcher , Wei Lu , Shaunak Chatterjee , Souvik Ghosh , Yang Li , Wei Deng , Qiang Wu
IPC分类号： G06F17/00 , G06F40/174 , G06Q50/00 , G06N20/00 , H04L29/08
摘要： In an example, first and second machine learned models corresponding to a particular context of a social networking service are obtained, the first machine learned model trained via a first machine learning algorithm to output an indication of importance of a social networking profile field to obtaining results in the particular context, and the second machine learned model trained via a second machine learning algorithm to output a propensity of the user to edit a social networking profile field if requested. One or more missing fields in a social networking profile for the user are identified. For each of one or more of the one or more missing fields, the field and an identification of the user are passed through the first and second machine learned models, and outputs of the first and second machine learned models are combined to identify one or more top missing profile fields.
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公开(公告)号：US11768874B2
公开(公告)日：2023-09-26
申请号：US16225888
申请日：2018-12-19
发明人： Yiming Ma , Xuhong Zhang , Wei Lu , Mingzhou Zhou
IPC分类号： G06F16/901 , G06F16/906 , G06N20/00
CPC分类号： G06F16/9017 , G06F16/906 , G06F16/9014 , G06N20/00
摘要： The disclosed embodiments provide a system for processing data. During operation, the system applies a first set of hash functions to a first entity identifier (ID) for a first entity to generate a first set of hash values. Next, the system produces a first set of intermediate vectors from the first set of hash values and a first set of lookup tables by matching each hash value in the first set of hash values to an entry in a corresponding lookup table in the first set of lookup tables. The system then performs an element-wise aggregation of the first set of intermediate vectors to produce a first embedding. Finally, the system outputs the first embedding for use by a machine learning model.
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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：US20190108209A1
公开(公告)日：2019-04-11
申请号：US15825657
申请日：2017-11-29
发明人： Karan Ashok Ahuja , Befekadu Ayenew Ejigou , Ningfeng Liang , Lokesh P. Bajaj , Wei Wang , Paul Fletcher , Wei Lu , Shaunak Chatterjee , Souvik Ghosh , Yang Li , Wei Deng , Qiang Wu
摘要： In an example, first and second machine learned models corresponding to a particular context of a social networking service are obtained, the first machine learned model trained via a first machine learning algorithm to output an indication of importance of a social networking profile field to obtaining results in the particular context, and the second machine learned model trained via a second machine learning algorithm to output a propensity of the user to edit a social networking profile field if requested. One or more missing fields in a social networking profile for the user are identified. For each of one or more of the one or more missing fields, the field and an identification of the user are passed through the first and second machine learned models, and outputs of the first and second machine learned models are combined to identify one or more top missing profile fields.
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