-
公开(公告)号：US11861469B2
公开(公告)日：2024-01-02
申请号：US16919258
申请日：2020-07-02
发明人： Peter Daniel Kirchner , Gregory Bramble , Horst Cornelius Samulowitz , Dakuo Wang , Arunima Chaudhary , Gregory Filla
摘要： An embodiment of the invention may include a method, computer program product, and system for creating a data analysis tool. The method may include a computing device that generates an AI pipeline based on an input dataset, wherein the AI pipeline is generated using an Automated Machine Learning program. The method may include converting the AI pipeline to a non-native format of the Automated Machine Learning program. This may enable the AI pipeline to be used outside of the Automated Machine Learning program, thereby increasing the usefulness of the created program by not tying it to the Automated Machine Learning program. Additionally, this may increase the efficiency of running the AI pipeline by eliminating unnecessary computations performed by the Automated Machine Learning program.
-
公开(公告)号：US20230289277A1
公开(公告)日：2023-09-14
申请号：US17692268
申请日：2022-03-11
IPC分类号： G06F11/34
CPC分类号： G06F11/3452 , G06F11/3428 , G06N20/00
摘要： Computer hardware and/or software that performs the following operations: (i) assessing a performance of a plurality of unsupervised machine learning pipelines against a plurality of data sets; (ii) associating the performance with meta-features corresponding to respective pipeline/data set combinations; (iii) training a supervised meta-learning model using the associated performance and meta-features as training data; and (iv) utilizing the trained model to identify one or more pipelines for processing an input data set.
-
公开(公告)号：US20220164698A1
公开(公告)日：2022-05-26
申请号：US17104642
申请日：2020-11-25
发明人： Arunima Chaudhary , Dakuo Wang , Abel Valente , Carolina Maria Spina , Hima Patel , Nitin Gupta , Gregory Bramble , Horst Cornelius Samulowitz , Sameep Mehta , Theodoros Salonidis , Daniel M. Gruen , Chaung Gan
摘要： A method to automatically assess data quality of data input into a machine learning model and remediate the data includes receiving input data for an automated machine learning model. Selections for a multiple data quality metrics are displayed. A selection for data quality metrics is received. The data quality metrics are determined according to the selection. Selections for data remediation strategies based on the selection of the data quality metrics are displayed. A selection for remediation recommendation strategies is received. The selected data remediation strategies are performed on the input data. Learning from the selection of the data quality metrics and the selection for the remediation strategies is performed. A new customized machine learning model is generated based on the learning.
-
公开(公告)号：US11275974B2
公开(公告)日：2022-03-15
申请号：US16133583
申请日：2018-09-17
IPC分类号： G06K9/22 , G06F16/90 , G06K9/62 , G06F16/901 , G06N20/00
摘要： Embodiments for automated feature engineering by one or more processors are described. One or more selected transformations may be applied to a set of features in a dataset to create a set of transform features using random feature transformation forest (RFTF) classifiers. A transform feature may be selected from the set of transform features having a highest discriminative power as compared to other features of the set of transform features. At each node in a decision tree, store the selected feature, a split value, and the one or more selected transformations for the transform feature.
-
公开(公告)号：US11048718B2
公开(公告)日：2021-06-29
申请号：US15673812
申请日：2017-08-10
发明人： Elias Khalil , Udayan Khurana , Fatemeh Nargesian , Horst Cornelius Samulowitz , Deepak S. Turaga
摘要： Embodiments for feature engineering by one or more processors are described. A plurality of transformations are applied to a set of features in each of a plurality of datasets. An output of each of the plurality of transformations is a score. For each of the sets of features, selecting those of the plurality of transformations for which said score is above a predetermined threshold. A signal representative of said selection is generated.
-
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.
-
公开(公告)号：US11868230B2
公开(公告)日：2024-01-09
申请号：US17692268
申请日：2022-03-11
CPC分类号： G06F11/3452 , G06F11/3428 , G06N20/00
摘要： Computer hardware and/or software that performs the following operations: (i) assessing a performance of a plurality of unsupervised machine learning pipelines against a plurality of data sets; (ii) associating the performance with meta-features corresponding to respective pipeline/data set combinations; (iii) training a supervised meta-learning model using the associated performance and meta-features as training data; and (iv) utilizing the trained model to identify one or more pipelines for processing an input data set.
-
公开(公告)号：US20230177032A1
公开(公告)日：2023-06-08
申请号：US17545880
申请日：2021-12-08
发明人： Daniel Karl I. Weidele , Lisa Amini , Udayan Khurana , Kavitha Srinivas , Horst Cornelius Samulowitz , Takaaki Tateishi , Carolina Maria Spina , Dakuo Wang , Abel Valente , Arunima Chaudhary , Toshihiro Takahashi
IPC分类号： G06F16/22 , G06F16/2457 , G06F16/28
CPC分类号： G06F16/221 , G06F16/2457 , G06F16/288 , G06F16/2282
摘要： A computer-implemented method according to one embodiment includes identifying a data set and meta information; and augmenting the data set with additional features in response to an automatic analysis of the data set in view of the meta information.
-
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.
-
公开(公告)号：US20230076089A1
公开(公告)日：2023-03-09
申请号：US17447126
申请日：2021-09-08
IPC分类号： G06F16/332 , G06F16/335 , G06K9/00
摘要： A method, system, and computer program product are disclosed. The method includes extracting at least one identifier from a formula in a document and extracting text passages in the document that contain the identifier(s). The method also includes selecting an identifier and extracted text passages containing the identifier, as well as generating identifier-passage pairs for the selected text passages and the identifier. Further, the method includes submitting the identifier-passage pairs to a question answering (QA) model, which generates candidate answers from the selected text passages. A definition of the identifier is then selected from the candidate answers.
-
-
-
-
-
-
-
-
-
搜索结果
30 条记录 (耗时 0.022 秒)
