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公开(公告)号：US20180365582A1
公开(公告)日：2018-12-20
申请号：US15624642
申请日：2017-06-15
Applicant: Microsoft Technology Licensing, LLC
Inventor： Madanlal S. MUSUVATHI , Todd D. MYTKOWICZ , Saeed MALEKI , Yufei DING
Abstract: Described herein is a system that transmits and combines local models, that individually comprise a set of local parameters computed via stochastic gradient descent (SGD), into a global model that comprises a set of global model parameters. The local models are computed in parallel at different geographic locations along with symbolic representations. Network transmission of the local models and the symbolic representations, rather than transmission of the large training data subsets processed to compute the local models and symbolic representations, conserves resources and decreases latency. The global model can then be used as a model to determine a likelihood of a course of action being successful for an organization. For example, the course of action can be a purchase of a security or a business operation strategy. In another example, the course of action can be a type of medical treatment for a patient.
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公开(公告)号：US20180367550A1
公开(公告)日：2018-12-20
申请号：US15624614
申请日：2017-06-15
Applicant: Microsoft Technology Licensing, LLC
Inventor： Madanlal S. MUSUVATHI , Todd D. MYTKOWICZ , Saeed MALEKI , Yufei DING
CPC classification number: H04L63/1416 , G06N7/005 , G06N20/00 , G06N20/20 , H04L63/0236 , H04L63/1425 , H04L63/1458
Abstract: Described herein is a system transmits and combines local models, that individually comprise a set of local parameters computed via stochastic gradient descent (SGD), into a global model that comprises a set of global model parameters. The local models are computed in parallel at different geographic locations (e.g., different instances of computing infrastructure) along with symbolic representations. Network transmission of the local models and the symbolic representations, rather than transmission of the large training data subsets processed to compute the local models and symbolic representations, conserves resources and decreases latency. The global model can then be used as a model to determine a likelihood that at least a portion of current and/or recently received data traffic is illegitimate data traffic that is associated with a cyber attack. In some instances, the system can implement a remedial action to mitigate the effects of the cyber attack on computing infrastructure.
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公开(公告)号：US20180365580A1
公开(公告)日：2018-12-20
申请号：US15624555
申请日：2017-06-15
Applicant: Microsoft Technology Licensing, LLC
Inventor： Madanlal S. MUSUVATHI , Todd D. MYTKOWICZ , Saeed MALEKI , Yufei DING
Abstract: Described herein is a system that transmits and combines local models, that individually comprise a set of local parameters computed via stochastic gradient descent (SGD), into a global model that comprises a set of global model parameters. The local models are computed in parallel at different geographic locations along with symbolic representations. The symbolic representations can be used to combine the local models. The global model can determine a likelihood, given a new data instance of a feature set, that a user performs a computer interaction with the content element. For instance, the system can use the model to provide search results in response to a search query submitted by a user. Or, the system can use the model to make a recommendation or suggestion to a user in response to a request for content (e.g., display a targeted advertisement, suggest a news story, etc.).
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公开(公告)号：US20180365093A1
公开(公告)日：2018-12-20
申请号：US15624660
申请日：2017-06-15
Applicant: Microsoft Technology Licensing, LLC
Inventor： Madanlal S. MUSUVATHI , Todd D. MYTKOWICZ , Saeed MALEKI , Yufei DING
Abstract: Described herein is a system that transmits and combines local models, that individually comprise a set of local parameters computed via stochastic gradient descent (SGD), into a global model that comprises a set of global model parameters. The local models are computed in parallel at different geographic locations along with symbolic representations. Network transmission of the local models and the symbolic representations, rather than transmission of the large training data subsets processed to compute the local models and symbolic representations, conserves resources and decreases latency. The global model can then be used as a model to determine a likelihood of a monitored resource or a user of the monitored resource experiencing a problem with respect to performance or completion of one or more operations. The system can also implement an action to assist in resolving or avoiding the problem.
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