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公开(公告)号：US20240185027A1
公开(公告)日：2024-06-06
申请号：US18074148
申请日：2022-12-02
Applicant: International Business Machines Corporation
Inventor： Deepak Vijaykeerthy , Nishtha Madaan , Swagatam Haldar , Aniya Aggarwal , Diptikalyan Saha
Abstract: Using encoded representations of target model training data and a label corresponding to each portion of the target model training data, a proxy model to determine an uncertainty score corresponding to an output of a trained target model is trained. Using the trained proxy model, a set of uncertainty scores is computed, each uncertainty score in the set of uncertainty scores corresponding to a portion of target model testing data in a set of target model testing data. A subset of the set of target model testing data is selected, the subset comprising a plurality of portions of target model testing data having an uncertainty score above a threshold uncertainty score. Using the subset of the set of target model testing data, the trained target model.
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公开(公告)号：US20220076144A1
公开(公告)日：2022-03-10
申请号：US17015243
申请日：2020-09-09
Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION
Inventor： Parikshit Ram , Dakuo Wang , Deepak Vijaykeerthy , Vaibhav Saxena , Sijia Liu , Arunima Chaudhary , Gregory Bramble , Horst Cornelius Samulowitz , Alexander Gray
Abstract: The exemplary embodiments disclose a method, a computer program product, and a computer system for determining that one or more model pipelines satisfy one or more constraints. The exemplary embodiments may include detecting a user uploading data, one or more constraints, and one or more model pipelines, collecting the data, the one or more constraints, and the one or more model pipelines, and determining that one or more of the model pipelines satisfies all of the one or more constraints based on applying one or more algorithms to the collected data, constraints, and model pipelines.
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公开(公告)号：US11636331B2
公开(公告)日：2023-04-25
申请号：US16506413
申请日：2019-07-09
Applicant: International Business Machines Corporation
Inventor： Deepak Vijaykeerthy , Philips George John , Diptikalyan Saha
Abstract: Methods, systems, and computer program products for active explanation guided learning are provided herein. A computer-implemented method includes identifying a subset of training examples, from a set of training examples, based on at least one of (i) an uncertainty metric computed for each one of the training examples and (ii) an influence metric computed for each one of the training examples; outputting said subset of training examples to a user; obtaining, from the user, a user explanation for each training example in said subset of training examples, wherein each of the user explanations identifies at least one part of the corresponding training example; and training a machine learning model based at least in part on the user explanations, wherein said training comprises prioritizing the identified parts of the training examples in the subset.
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公开(公告)号：US11500671B2
公开(公告)日：2022-11-15
申请号：US16510534
申请日：2019-07-12
Applicant: International Business Machines Corporation
Inventor： Evelyn Duesterwald , Anupama Murthi , Deepak Vijaykeerthy , Vijay Arya , Ganesh Venkataraman
Abstract: In an embodiment, a method for inspecting and transforming a machine learning model includes receiving a request that includes the machine learning model and a configuration object that provides an indication of a selected strategy. In the embodiment, the method includes creating a partially specified task graph that includes a first placeholder node for a future expanded task node. In the embodiment, the method includes performing a dynamic expansion and execution phase that includes, repeatedly (a) using a cognitive engine to evaluate whether to revise the partially specified task graph based at least in part on the selected strategy, and (b) using a processor-based execution engine to perform an action specified by the complete node. In an embodiment, the dynamic expansion and execution phase repeats until after the cognitive engine adds a consolidated results node.
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公开(公告)号：US10733287B2
公开(公告)日：2020-08-04
申请号：US15978868
申请日：2018-05-14
Applicant: International Business Machines Corporation
Inventor： Manish Kesarwani , Suranjana Samanta , Deepak Vijaykeerthy , Sameep Mehta , Karthik Sankaranarayanan
Abstract: One embodiment provides a method, including: deploying a machine learning model, wherein the deployed machine learning model is used in responding to queries from users; receiving, at the deployed machine learning model, input from a user; identifying a type of machine learning model attack corresponding to the received input; computing, responsive to receiving the input, a resiliency score of the machine learning model, wherein the resiliency score indicates resistance of the machine learning model against the identified type of attack; and performing an action responsive to the computed resiliency score.
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Patent Agency Ranking
公开(公告)号：US20200234184A1
公开(公告)日：2020-07-23
申请号：US16255620
申请日：2019-01-23
Applicant: International Business Machines Corporation
Inventor： Manish Kesarwani , Deepak Vijaykeerthy , Sameep Mehta , Suranjana Samanta , Karthik Sankaranarayanan
IPC: G06N20/00 , G06F16/903
Abstract: One embodiment provides a method, including: deploying a machine learning model, wherein the machine learning model is used in responding to queries from users; receiving, at the deployed machine learning model, input from at least one entity; determining that the at least one entity is an adversary attempting to retrain and/or steal the deployed machine learning model; and providing, in view of the determining that the at least one entity is an adversary, an altered response, wherein the altered response comprises at least one of: a response from a machine learning model other than the deployed machine learning model and a response from the deployed machine learning model altered with errors.
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公开(公告)号：US12242980B2
公开(公告)日：2025-03-04
申请号：US17015243
申请日：2020-09-09
Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION
Inventor： Parikshit Ram , Dakuo Wang , Deepak Vijaykeerthy , Vaibhav Saxena , Sijia Liu , Arunima Chaudhary , Gregory Bramble , Horst Cornelius Samulowitz , Alexander Gray
IPC: G06N5/04 , G06F9/38 , G06F18/243
Abstract: The exemplary embodiments disclose a method, a computer program product, and a computer system for determining that one or more model pipelines satisfy one or more constraints. The exemplary embodiments may include detecting a user uploading data, one or more constraints, and one or more model pipelines, collecting the data, the one or more constraints, and the one or more model pipelines, and determining that one or more of the model pipelines satisfies all of the one or more constraints based on applying one or more algorithms to the collected data, constraints, and model pipelines.
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8.发明公开公开(公告)号：US20240194184A1
公开(公告)日：2024-06-13
申请号：US18062857
申请日：2022-12-07
Applicant: International Business Machines Corporation
Inventor： Swagatam Haldar , Diptikalyan Saha , Deepak Vijaykeerthy , Aniya Aggarwal , Nishtha Madaan
CPC classification number: G10L15/01 , G06N3/08 , G10L15/1815 , G10L15/32 , G10L25/18
Abstract: One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to a process to facilitate testing a cascaded pipeline. 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 input component, a cascaded pipeline, and an evaluation component. The input component can receive a test case associated with a label from labeled speech data represented by waveform. The evaluation component can feed the test case to the cascaded pipeline to obtain an output of the cascaded pipeline. The evaluation component can evaluate a robustness of the cascaded pipeline by comparing the output of the cascaded pipeline and the label. The cascaded pipeline can include a first model and a second model, and the first model can be different than the second model.
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公开(公告)号：US20220335217A1
公开(公告)日：2022-10-20
申请号：US17233727
申请日：2021-04-19
Applicant: International Business Machines Corporation
Inventor： Naveen Panwar , Nishtha Madaan , Deepak Vijaykeerthy , Pranay Kumar Lohia , Diptikalyan Saha
IPC: G06F40/279 , G06N3/08 , G06N3/04 , G06K9/62
Abstract: Methods, systems, and computer program products for detecting contextual bias in text are provided herein. A computer-implemented method includes identifying, by a machine learning network, a protected attribute in one or more data samples; processing the identified data samples using a first sub-network of the machine learning network, wherein the first sub-network is configured to determine a plurality of contexts of the protected attribute across the identified data samples; determining an impact of each of the plurality of contexts on a second sub-network of the machine learning network, wherein the second sub-network of the machine learning network is configured to classify a given data sample into one of a plurality of classes; and adjusting the second sub-network of the machine learning to account for the impact of at least one of the plurality of contexts on the second sub-network.
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公开(公告)号：US20210012156A1
公开(公告)日：2021-01-14
申请号：US16506413
申请日：2019-07-09
Applicant: International Business Machines Corporation
Inventor： Deepak Vijaykeerthy , Philips George John , Diptikalyan Saha
Abstract: Methods, systems, and computer program products for active explanation guided learning are provided herein. A computer-implemented method includes identifying a subset of training examples, from a set of training examples, based on at least one of (i) an uncertainty metric computed for each one of the training examples and (ii) an influence metric computed for each one of the training examples; outputting said subset of training examples to a user; obtaining, from the user, a user explanation for each training example in said subset of training examples, wherein each of the user explanations identifies at least one part of the corresponding training example; and training a machine learning model based at least in part on the user explanations, wherein said training comprises prioritizing the identified parts of the training examples in the subset.
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