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公开(公告)号：US11966340B2
公开(公告)日：2024-04-23
申请号：US17654965
申请日：2022-03-15
发明人： Long Vu , Bei Chen , Xuan-Hong Dang , Peter Daniel Kirchner , Syed Yousaf Shah , Dhavalkumar C. Patel , Si Er Han , Ji Hui Yang , Jun Wang , Jing James Xu , Dakuo Wang , Gregory Bramble , Horst Cornelius Samulowitz , Saket K. Sathe , Wesley M. Gifford , Petros Zerfos
IPC分类号： G06F12/0871 , G06N20/00
CPC分类号： G06F12/0871 , G06N20/00 , G06F2212/604
摘要： To automate time series forecasting machine learning pipeline generation, a data allocation size of time series data may be determined based on one or more characteristics of a time series data set. The time series data may be allocated for use by candidate machine learning pipelines based on the data allocation size. Features for the time series data may be determined and cached by the candidate machine learning pipelines. Predictions of each of the candidate machine learning pipelines using at least the one or more features may be evaluated. A ranked list of machine learning pipelines may be automatically generated from the candidate machine learning pipelines for time series forecasting based upon evaluating predictions of each of the one or more candidate machine learning pipelines.
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公开(公告)号：US11836483B1
公开(公告)日：2023-12-05
申请号：US17804322
申请日：2022-05-27
发明人： Jun Wang , Dong Hai Yu , Bo Song , Rui Wang , Yao Dong Liu , Jiang Bo Kang
摘要： Described are techniques for machine learning library management. The techniques include generating a table including a plurality of machine learning libraries and their current versions that are used in a deployed machine learning platform (MLP) instance, a first available version upgrade for a first machine learning library of the plurality of machine learning libraries, a security indication associated with the first available version upgrade relative to a current version implemented by the first machine learning library, and a compatibility indication between the first available version upgrade and the current version of the first machine learning library. The techniques further include generating a recommendation related to upgrading the first machine learning library based on the security indication and the compatibility indication.
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3.发明公开公开(公告)号：US20230359941A1
公开(公告)日：2023-11-09
申请号：US17739716
申请日：2022-05-09
发明人： Dong Hai Yu , Jun Wang , Bo Song , Yao Dong Liu , Jiang Bo Kang , Lei Tian , XING WEI
CPC分类号： G06N20/20 , G06Q20/4016
摘要： A computer-implemented system, platform, programing product, and/or method for improving transformation selection in an ensemble machine learning (ML) model that includes: providing all base ML models of the ensemble ML model; identifying all of a plurality of Derived Fields in all the base ML models; performing a Derived Field run prediction analysis for all the Derived Fields; computing the Derived Field Importance Weight for Field (DFIW4F) and the Derived Field Importance Weight for Model (DFIW4M) for all the Derived Fields; clustering all the Derived Fields into a plurality of Derived Field clusters, wherein each Derived Field cluster is based upon the DFIW4M and the DFIW4F for the Derived Field; sorting all the Derived Field clusters by best cluster based upon DFIW4M and DFIW4F; and running the base ML models based upon the Derived Fields in the best Derived Field cluster until sufficient base ML models have been run.
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4.发明公开公开(公告)号：US20230289693A1
公开(公告)日：2023-09-14
申请号：US17654612
申请日：2022-03-14
发明人： Wen Pei Yu , Xiao Ming Ma , Xue Ying Zhang , Si Er Han , Jing James Xu , Jing Xu , Rui Wang , Jun Wang , Ji Hui Yang
CPC分类号： G06Q10/06375 , G06F11/3457
摘要： A method, computer system, and a computer program product for performing an interactive outcome analysis is provided. The present invention may include generating, by a computer, a first estimation outcome from a first plurality of input conditions. The present invention may include generating, by the computer, a parallel estimation outcome from a second plurality of input conditions, wherein at least one of said input conditions in said first plurality of input conditions is different from any of said second plurality of input conditions. The present invention may include selecting, by the computer, either said first or said parallel estimation outcome by analyzing said outcomes with one another and with a target goal outcome.
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公开(公告)号：US20230214454A1
公开(公告)日：2023-07-06
申请号：US17568305
申请日：2022-01-04
发明人： Ke Wei Wei , Jun Wang , Shuang YS Yu , Guang Ming Zhang , Yuan Feng , Yi Dai , Ling Zhuo , Jing Xu
CPC分类号： G06K9/6257 , G06K9/6263 , G06K9/6219 , G06N20/20
摘要： An embodiment generates an initial set of training data from monitoring data. The initial set of training data is generated by combining outputs from a plurality of pretrained classifiers. The embodiment trains a new classification model using the initial set of training data to identify anomalies in monitoring data. The embodiment performs a multiple-level clustering of the data samples resulting in a plurality of clusters of sub-clusters of data samples, and generates a review list of data samples by selecting a representative data sample from each of the clusters. The embodiment receives an updated data sample from the expert review that includes a revised target classification for at least one of the data samples of the expert review list. The embodiment then trains another replacement classification model using a revised set of training data that includes the updated data sample and associated revised target classification.
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公开(公告)号：US20230138987A1
公开(公告)日：2023-05-04
申请号：US17453565
申请日：2021-11-04
发明人： Song Bo , Dong Hai Yu , Jun Wang , Jiang Bo Kang , Yao Dong Liu
摘要： One or more computer processors calculate a cache prediction for a received inference request within an inference cache structured as a self-learning tree, wherein the inference request comprises a set of input values. The one or more computer processors responsive to the retrieved cache prediction exceeding a cache prediction threshold, transmit the cache prediction. The one or more computer processors parallel compute a model prediction for the received inference request utilizing a trained model. The one or more computer processors responsive to the retrieved model prediction exceeding a model prediction threshold, convert the trained model into a tree structure. The one or more computer processors update the inference cache with the converted train model. The one or more computer processors transmit the model prediction.
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公开(公告)号：US11620582B2
公开(公告)日：2023-04-04
申请号：US16942247
申请日：2020-07-29
发明人： Bei Chen , Long Vu , Syed Yousaf Shah , Xuan-Hong Dang , Peter Daniel Kirchner , Si Er Han , Ji Hui Yang , Jun Wang , Jing James Xu , Dakuo Wang , Dhavalkumar C. Patel , Gregory Bramble , Horst Cornelius Samulowitz , Saket Sathe , Chuang Gan
IPC分类号： G06N20/20
摘要： Techniques regarding one or more automated machine learning processes that analyze time series data are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a time series analysis component that selects a machine learning pipeline for meta transfer learning on time series data by sequentially allocating subsets of training data from the time series data amongst a plurality of machine learning pipeline candidates.
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公开(公告)号：US20220224722A1
公开(公告)日：2022-07-14
申请号：US17148684
申请日：2021-01-14
发明人： Sheng Yan Sun , Shuo Li , Xiaobo Wang , Jun Wang , Hua Wang , Shidong Shan , Xing Xing Jing
摘要： A method, system, and computer program product for recommending an initial database security model. The method may include identifying a plurality of nodes connected to a security network. The method may also include analyzing security characteristics of each node of the plurality of nodes. The method may also include identifying, from the security characteristics, key factors for each node. The method may also include calculating similarities between each node of the plurality of nodes. The method may also include building a self-organized centerless network across the plurality of nodes by grouping nodes with high similarities based on the similarities between each node, where the self-organized centerless network is a centerless network without a central management server, and includes groups of nodes from the plurality of nodes. The method may also include generating federated security models for the groups of nodes.
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公开(公告)号：US20220091964A1
公开(公告)日：2022-03-24
申请号：US16948520
申请日：2020-09-22
发明人： Yao Dong Liu , Jing James Xu , Jiang Bo Kang , Dong Hai Yu , Jun Wang
IPC分类号： G06F11/36
摘要： Embodiments of the present invention disclose a method, computer program product, and system for estimating the results of a performance test on an updated software application. A method, the method comprising receiving an updated software application, wherein the size of the updated software application is a first size and generating a plurality of small probe, wherein the size of each of the small probe data is a second size, wherein the second size is less than the first size. Conducting a first performance test on the plurality of small probe data and calculating an estimated elapsed time for a performance test on the updated software application. Conducting the performance test on the updated software application and determining if the updated software is given a PASS or FAIL for the performance test, based in part on the elapsed time of the performance test on the updated software application.
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公开(公告)号：US20220036610A1
公开(公告)日：2022-02-03
申请号：US16942284
申请日：2020-07-29
发明人： Dakuo Wang , Bei Chen , Ji Hui Yang , Abel Valente , Arunima Chaudhary , Chuang Gan , John Dillon Eversman , Voranouth Supadulya , Daniel Karl I. Weidele , Jun Wang , Jing James Xu , Dhavalkumar C. Patel , Long Vu , Syed Yousaf Shah , Si Er Han
IPC分类号： G06T11/20 , G06F3/0481
摘要： Systems, computer-implemented methods, and computer program products to facilitate visualization of a model selection process are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an interaction backend handler component that obtains one or more assessment metrics of a model pipeline candidate. The computer executable components can further comprise a visualization render component that renders a progress visualization of the model pipeline candidate based on the one or more assessment metrics.
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