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公开(公告)号：US20190172564A1
公开(公告)日：2019-06-06
申请号：US15831426
申请日：2017-12-05
Applicant: International Business Machines Corporation
Inventor： Rachita Chandra , Vijay S. Iyengar , Dmitriy A. Katz , Karthikeyan Natesan Ramamurthy , Emily A. Ray , Moninder Singh , Dennis Wei , Gigi Y. C. Yuen-Reed , Kevin N. Tran
Abstract: A system may predict costs for a set of members by building and using a predictive pipeline. The pipeline may be built using a set of historical data for training members. A set of member-level features can be identified by performing empirical testing on the set of historical data. The trained configurable predictive pipeline can generate a set of predictive data for each member, using historical test data for a set of testing members. The system can then generate a predictive report for each set of predictive data.
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公开(公告)号：US12106193B2
公开(公告)日：2024-10-01
申请号：US17308310
申请日：2021-05-05
Applicant: International Business Machines Corporation
Inventor： Oznur Alkan , Elizabeth Daly , Rahul Nair , Massimiliano Mattetti , Dennis Wei , Karthikeyan Natesan Ramamurthy
Abstract: Embodiments are disclosed for a method. The method includes receiving feedback decision rules for multiple predictions by a trained machine learning model. generating a feedback rule set based on the feedback decision rules. The method further includes generating an updated training dataset based on an original training dataset and an updated feedback rule set. The updated feedback rule set resolves one or more conflicts of the feedback rule set, and the updated training dataset is configured to train the machine learning model to move a decision boundary. Generating the updated training dataset includes generating multiple updated training instances by applying one of the feedback decision rules to a training instance of the original training dataset.
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公开(公告)号：US20240168940A1
公开(公告)日：2024-05-23
申请号：US18057776
申请日：2022-11-22
Applicant: International Business Machines Corporation
IPC: G06F16/23 , G06F16/2453
CPC classification number: G06F16/2365 , G06F16/2453
Abstract: One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to a process for providing an explanation result for an analytical model. A system can comprise a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components can comprise an uncertainty component that determines an uncertainty score for a distribution of samples that neighbor a selected input to an analytical model, a sampling component that identifies a subset of the distribution of samples based on the uncertainty score, and an explanation component that generates an explanation of an output of the analytical model, corresponding to the selected input, based on use of a sample from the subset of the distribution of samples.
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公开(公告)号：US11734585B2
公开(公告)日：2023-08-22
申请号：US16214703
申请日：2018-12-10
Applicant: International Business Machines Corporation
Inventor： Manish Bhide , Pranay Lohia , Karthikeyan Natesan Ramamurthy , Ruchir Puri , Diptikalyan Saha , Kush Raj Varshney
Abstract: A post-processing method, system, and computer program product for post-hoc improvement of instance-level and group-level prediction metrics, including training a bias detector that learns to detect a sample that has an individual bias greater than a predetermined individual bias threshold value with constraints on a group bias, applying the bias detector on a run-time sample to select a biased sample in the run-time sample having a bias greater than the predetermined individual bias threshold bias value, and suggesting a de-biased prediction for the biased sample.
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公开(公告)号：US20190333155A1
公开(公告)日：2019-10-31
申请号：US15964856
申请日：2018-04-27
Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION
Inventor： Karthikeyan Natesan Ramamurthy , Emily A. Ray , Dennis Wei , Gigi Y.C. Yuen-Reed
Abstract: A method, computer system, and a computer program product for generating and reporting a plurality of health insurance cost predictions via private transfer learning is provided. The present invention may include retrieving a set of source data, and a set of target data. The present invention may then include creating and anonymizing a plurality of source data sets, and at least one target data set. The present invention may further include generating one or more source learner models, and a target learner model. The present invention may then include combining the one or more generated source learner models and the generated target learner model to generate a transfer learner. The present invention may further include generating a prediction based on the generated transfer learner.
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Patent Agency Ranking
16.发明授权公开(公告)号：US09569874B2
公开(公告)日：2017-02-14
申请号：US14732345
申请日：2015-06-05
Applicant: International Business Machines Corporation
Inventor： Chung-Ching Lin , Sharathchandra U. Pankanti , Karthikeyan Natesan Ramamurthy , Aleksandr Y. Aravkin , John R. Smith
CPC classification number: G06T11/60 , G06K9/0063 , G06K9/4609 , G06K9/4638 , G06K9/4671 , G06K9/6211 , G06K2009/2045 , G06K2009/6213 , G06T3/4038 , G06T5/006 , G06T7/33 , G06T2207/10032 , G06T2207/20104 , G06T2207/20221
Abstract: A method and a system of stitching a plurality of views of a scene, the method including extracting points of interest in each view to include a point set from each of the plurality of image views of the scene, matching the points of interest and reducing an outlier, grouping the matched points of interest in a plurality of groups, determining a similarity transformation with a smallest rotation angle for each grouping of the match points, generating virtual matching points on a non-overlapping area of the plurality of image views, generating virtual matching points on an overlapping area for each of the plurality of image views, and calculating piecewise projective transformations for the plurality of image views.
Abstract translation: 一种缝合场景的多个视图的方法和系统，所述方法包括提取每个视图中的兴趣点以包括来自所述场景的所述多个图像视图中的每一个的点集合，以匹配所述兴趣点并减少 异常值，将匹配的兴趣点分组在多个组中，为每个匹配点的分组确定具有最小旋转角度的相似度变换，在多个图像视图的非重叠区域上生成虚拟匹配点，生成虚拟 对于多个图像视图中的每一个，在重叠区域上匹配点，以及对多个图像视图计算分段投影变换。
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公开(公告)号：US20230395202A1
公开(公告)日：2023-12-07
申请号：US17805481
申请日：2022-06-06
Applicant: International Business Machines Corporation
Inventor： Payel Das , Yair Zvi Schiff , Enara C. Vijil , Samuel Chung Hoffman , Karthikeyan Natesan Ramamurthy
Abstract: Embodiments of the invention provide a computer-implemented method that includes applying input representations of a three-dimensional (3D) domain to a generative neural network (GNN); and using the GNN to form a generative model of the 3D domain based at least in part on the input representations. The input representations include a global-shape input representation of the 3D domain.
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公开(公告)号：US20230306078A1
公开(公告)日：2023-09-28
申请号：US17655803
申请日：2022-03-22
Applicant: International Business Machines Corporation
Inventor： Oznur Alkan , Elizabeth Daly , Karthikeyan Natesan Ramamurthy , Skyler SPEAKMAN
IPC: G06K9/62
CPC classification number: G06K9/6257 , G06K9/6263 , G06K9/6231
Abstract: A computer-implemented method, a computer program product, and a computer system for designing a fair machine learning model through user interaction. A computer system receives from a user a request for reviewing one or more biased subgroups in a dataset used in training a machine learning model and presents to the user the one or more biased subgroups and respective bias scores thereof. A computer system preprocesses the dataset to mitigate bias, in response to receiving from the user a request for mitigating the bias associated with the one or more biased subgroups. A computer system retrains the machine learning model, using a new dataset obtained from preprocessing the dataset. A computer system presents to the user respective new bias scores of the one or more biased subgroups in the new dataset. The user reviews the respective new bias scores to determine whether the fair machine learning model is built.
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公开(公告)号：US20230229943A1
公开(公告)日：2023-07-20
申请号：US18125193
申请日：2023-03-23
Applicant: international Business Machines Corporation
Inventor： Manish Bhide , Pranay Lohia , Karthikeyan Natesan Ramamurthy , Ruchir Puri , Diptikalyan Saha , Kush Raj Varshney
Abstract: A post-processing method, system, and computer program product for post-hoc improvement of instance-level and group-level prediction metrics, including training a bias detector on a payload data that learns to detect a sample in a customer model that has an individual bias greater than a predetermined individual bias threshold value with constraints on a group bias, suggesting, in the run-time, a de-biased prediction based on the selected biased sample by a de-biasing procedure, and an arbiter decides based on user feedback whether to use the de-biased prediction or an original prediction made prior to the de-biasing procedure from the customer model which is then used as an output.
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公开(公告)号：US11568282B2
公开(公告)日：2023-01-31
申请号：US16702817
申请日：2019-12-04
Applicant: International Business Machines Corporation
Inventor： Pin-Yu Chen , Payel Das , Karthikeyan Natesan Ramamurthy , Pu Zhao
Abstract: Techniques for sanitization of machine learning (ML) models are provided. A first ML model is received, along with clean training data. A path is trained between the first ML model and a second ML model using the clean training data. A sanitized ML model is generated based on at least one point on the trained path. One or more ML functionalities are then facilitated using the sanitized ML model.
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