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公开(公告)号：US20180357566A1
公开(公告)日：2018-12-13
申请号：US15621753
申请日：2017-06-13
Applicant: Microsoft Technology Licensing, LLC
Inventor： Yu Liu , Jianshu Chen , Li Deng
IPC: G06N99/00 , G10L15/06 , G10L15/197 , G06N5/04
CPC classification number: G06N20/00 , G06N7/005 , G10L15/063 , G10L15/197
Abstract: In classification tasks applicable to data that exhibit sequential output statistics, a classifier may be trained in an unsupervised manner based on a sequence of input samples and an unaligned sequence of output labels, using a cost function that measures the negative cross-entropy of an N-gram joint probability distribution derived from the sequence of output labels with respect to an expected N-gram frequency in a second sequence of output labels predicted by the classifier. In some embodiments, a primal-dual reformulation of the cost function is employed to facilitate optimization.
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公开(公告)号：US10909450B2
公开(公告)日：2021-02-02
申请号：US15084113
申请日：2016-03-29
Applicant: Microsoft Technology Licensing, LLC
Inventor： Jianshu Chen , Li Deng , Jianfeng Gao , Xiadong He , Lihong Li , Ji He , Mari Ostendorf
Abstract: A processing unit can determine a first feature value corresponding to a session by operating a first network computational model (NCM) based part on information of the session. The processing unit can determine respective second feature values corresponding to individual actions of a plurality of actions by operating a second NCM. The second NCM can use a common set of parameters in determining the second feature values. The processing unit can determine respective expectation values of some of the actions of the plurality of actions based on the first feature value and the respective second feature values. The processing unit can select a first action of the plurality of actions based on at least one of the expectation values. In some examples, the processing unit can operate an NCM to determine expectation values based on information of a session and information of respective actions.
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公开(公告)号：US10474950B2
公开(公告)日：2019-11-12
申请号：US14754474
申请日：2015-06-29
Applicant: Microsoft Technology Licensing, LLC
Inventor： Xiaodong He , Jianshu Chen , Brendan W L Clement , Li Deng , Jianfeng Gao , Bochen Jin , Prabhdeep Singh , Sandeep P. Solanki , LuMing Wang , Hanjun Xian , Yilei Zhang , Mingyang Zhao , Zijian Zheng
Abstract: A processing unit can acquire datasets from respective data sources, each having a respective unique data domain. The processing unit can determine values of a plurality of features based on the plurality of datasets. The processing unit can modify input-specific parameters or history parameters of a computational model based on the values of the features. In some examples, the processing unit can determine an estimated value of a target feature based at least in part on the modified computational model and values of one or more reference features. In some examples, the computational model can include neural networks for several input sets. An output layer of at least one of the neural networks can be connected to the respective hidden layer(s) of one or more other(s) of the neural networks. In some examples, the neural networks can be operated to provide transformed feature value(s) for respective times.
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公开(公告)号：US10445650B2
公开(公告)日：2019-10-15
申请号：US14949156
申请日：2015-11-23
Applicant: Microsoft Technology Licensing, LLC
Inventor： Jianfeng Gao , Li Deng , Xiaodong He , Lin Xiao , Xinying Song , Yelong Shen , Ji He , Jianshu Chen
IPC: G06N7/00
Abstract: A processing unit can successively operate layers of a multilayer computational graph (MCG) according to a forward computational order to determine a topic value associated with a document based at least in part on content values associated with the document. The processing unit can successively determine, according to a reverse computational order, layer-specific deviation values associated with the layers based at least in part on the topic value, the content values, and a characteristic value associated with the document. The processing unit can determine a model adjustment value based at least in part on the layer-specific deviation values. The processing unit can modify at least one parameter associated with the MCG based at least in part on the model adjustment value. The MCG can be operated to provide a result characteristic value associated with test content values of a test document.
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公开(公告)号：US11170293B2
公开(公告)日：2021-11-09
申请号：US14985017
申请日：2015-12-30
Applicant: Microsoft Technology Licensing, LLC
Inventor： Jianfeng Gao , Li Deng , Xiaodong He , Prabhdeep Singh , Lihong Li , Jianshu Chen , Xiujun Li , Ji He
Abstract: A processing unit can operate a first recurrent computational model (RCM) to provide first state information and a predicted result value. The processing unit can operating a first network computational model (NCM) to provide respective expectation values of a plurality of actions based at least in part on the first state information. The processing unit can provide an indication of at least one of the plurality of actions, and receive a reference result value, e.g., via a communications interface. The processing unit can train the first RCM based at least in part on the predicted result value and the reference result value to provide a second RCM, and can train the first NCM based at least in part on the first state information and the at least one of the plurality of actions to provide a second NCM.
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Patent Agency Ranking
公开(公告)号：US10133729B2
公开(公告)日：2018-11-20
申请号：US14839281
申请日：2015-08-28
Applicant: Microsoft Technology Licensing, LLC
Inventor： Xiaodong He , Jianfeng Gao , Hamid Palangi , Xinying Song , Yelong Shen , Li Deng , Jianshu Chen
Abstract: Systems, methods, and computer-readable media for providing semantically-relevant discovery of solutions are described herein. In some examples, a computing device can receive an input, such as a query. The computing device can process each word of the input sequentially to determine a semantic representation of the input. Techniques and technologies described herein determine a response to the input, such as an answer, based on the semantic representation of the input matching a semantic representation of the response. An output including one or more relevant responses to the request can then be provided to the requestor. Example techniques described herein can apply machine learning to train a model with click-through data to provide semantically-relevant discovery of solutions. Example techniques described herein can apply recurrent neural networks (RNN) and/or long short term memory (LSTM) cells in the machine learning model.
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公开(公告)号：US20160379112A1
公开(公告)日：2016-12-29
申请号：US14754474
申请日：2015-06-29
Applicant: Microsoft Technology Licensing, LLC
Inventor： Xiaodong He , Jianshu Chen , Brendan WL Clement , Li Deng , Jianfeng Gao , Bochen Jin , Prabhdeep Singh , Sandeep P. Solanki , LuMing Wang , Hanjun Xian , Yilei Zhang , Mingyang Zhao , Zijian Zheng
CPC classification number: G06N3/08 , G06N3/0454 , G06N3/049 , G06N20/00
Abstract: A processing unit can acquire datasets from respective data sources, each having a respective unique data domain. The processing unit can determine values of a plurality of features based on the plurality of datasets. The processing unit can modify input-specific parameters or history parameters of a computational model based on the values of the features. In some examples, the processing unit can determine an estimated value of a target feature based at least in part on the modified computational model and values of one or more reference features. In some examples, the computational model can include neural networks for several input sets. An output layer of at least one of the neural networks can be connected to the respective hidden layer(s) of one or more other(s) of the neural networks. In some examples, the neural networks can be operated to provide transformed feature value(s) for respective times.
Abstract translation: 处理单元可以从相应的数据源获取数据集，每个数据源具有相应的唯一数据域。 处理单元可以基于多个数据集来确定多个特征的值。 处理单元可以基于特征的值修改计算模型的输入特定参数或历史参数。 在一些示例中，处理单元可以至少部分地基于修改的计算模型和一个或多个参考特征的值来确定目标特征的估计值。 在一些示例中，计算模型可以包括用于多个输入集合的神经网络。 至少一个神经网络的输出层可以连接到神经网络的一个或多个其他神经网络的相应隐藏层。 在一些示例中，可以操作神经网络以在相应时间提供变换的特征值。
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公开(公告)号：US10776716B2
公开(公告)日：2020-09-15
申请号：US15621753
申请日：2017-06-13
Applicant: Microsoft Technology Licensing, LLC
Inventor： Yu Liu , Jianshu Chen , Li Deng
IPC: G06N20/00 , G10L15/06 , G10L15/197 , G06N7/00
Abstract: In classification tasks applicable to data that exhibit sequential output statistics, a classifier may be trained in an unsupervised manner based on a sequence of input samples and an unaligned sequence of output labels, using a cost function that measures the negative cross-entropy of an N-gram joint probability distribution derived from the sequence of output labels with respect to an expected N-gram frequency in a second sequence of output labels predicted by the classifier. In some embodiments, a primal-dual reformulation of the cost function is employed to facilitate optimization.
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公开(公告)号：US10264081B2
公开(公告)日：2019-04-16
申请号：US14806281
申请日：2015-07-22
Applicant: Microsoft Technology Licensing, LLC
Inventor： Chenlei Guo , Jianfeng Gao , Xinying Song , Byungki Byun , Yelong Shen , Ye-Yi Wang , Brian D. Remick , Edward Thiele , Mohammed Aatif Ali , Marcus Gois , Xiaodong He , Jianshu Chen , Divya Jetley , Stephen Friesen
Abstract: Techniques for providing a people recommendation system for predicting and recommending relevant people (or other entities) to include in a conversation based on contextual indicators. In an exemplary embodiment, email recipient recommendations may be suggested based on contextual signals, e.g., project names, body text, existing recipients, current date and time, etc. In an aspect, a plurality of properties including ranked key phrases are associated with profiles corresponding to personal entities. Aggregated profiles are analyzed using first- and second-layer processing techniques. The recommendations may be provided to the user reactively, e.g., in response to a specific query by the user to the people recommendation system, or proactively, e.g., based on the context of what the user is currently working on, in the absence of a specific query by the user.
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公开(公告)号：US20170286860A1
公开(公告)日：2017-10-05
申请号：US15084113
申请日：2016-03-29
Applicant: Microsoft Technology Licensing, LLC
Inventor： Jianshu Chen , Li Deng , Jianfeng Gao , Xiadong He , Lihong Li , Ji He , Mari Ostendorf
IPC: G06N99/00
CPC classification number: G06N3/08 , G06N3/0454
Abstract: A processing unit can determine a first feature value corresponding to a session by operating a first network computational model (NCM) based part on information of the session. The processing unit can determine respective second feature values corresponding to individual actions of a plurality of actions by operating a second NCM. The second NCM can use a common set of parameters in determining the second feature values. The processing unit can determine respective expectation values of some of the actions of the plurality of actions based on the first feature value and the respective second feature values. The processing unit can select a first action of the plurality of actions based on at least one of the expectation values. In some examples, the processing unit can operate an NCM to determine expectation values based on information of a session and information of respective actions.
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