Patents by Inventor Parijat Dube
Parijat Dube has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 11853877Abstract: Whether to train a new neural network model can be determined based on similarity estimates between a sample data set and a plurality of source data sets associated with a plurality of prior-trained neural network models. A cluster among the plurality of prior-trained neural network models can be determined. A set of training data based on the cluster can be determined. The new neural network model can be trained based on the set of training data.Type: GrantFiled: April 2, 2019Date of Patent: December 26, 2023Assignee: International Business Machines CorporationInventors: Patrick Watson, Bishwaranjan Bhattacharjee, Siyu Huo, Noel Christopher Codella, Brian Michael Belgodere, Parijat Dube, Michael Robert Glass, John Ronald Kender, Matthew Leon Hill
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Publication number: 20230326186Abstract: An automated data labeling method, system, and computer program product that includes composing a semantically-named anchor vector derived from a source dataset into a sequence that defines a location description for target data items based on a generalization of distances into Cayley-Menger content and outputting a label for a target data item based on the location description.Type: ApplicationFiled: March 28, 2022Publication date: October 12, 2023Inventors: Parijat Dube, John Ronald Kender, Bishwaranjan Bhattacharjee, Brian Michael Belgodere
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Patent number: 11727309Abstract: Techniques for estimating runtimes of one or more machine learning tasks are provided. For example, one or more embodiments described herein can regard a system that can comprise a memory that stores 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 an extraction component that can extract a parameter from a machine learning task. The parameter can define a performance characteristic of the machine learning task. Also, the computer executable components can comprise a model component that can generate a model based on the parameter. Further, the computer executable components can comprise an estimation component that can generate an estimated runtime of the machine learning task based on the model.Type: GrantFiled: October 28, 2021Date of Patent: August 15, 2023Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Parijat Dube, Gauri Joshi, Priya Ashok Nagpurkar, Stefania Costache, Diana Jeanne Arroyo, Zehra Noman Sura
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Patent number: 11604961Abstract: A neural network models fragmenting method, system, and computer program product include recursively factoring out common prefixes of models, constructing a hierarchy of decomposed model fragments based on the factoring, and grouping the constructed hierarchy for deployment.Type: GrantFiled: April 30, 2019Date of Patent: March 14, 2023Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Vinod Muthusamy, Parijat Dube, Kaoutar El Maghraoui, Falk Pollok
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Patent number: 11568235Abstract: Embodiments for implementing mixed precision learning for neural networks by a processor. A neural network may be replicated into a plurality of replicated instances and each of the plurality of replicated instances differ in precision used for representing and determining parameters of the neural network. Data instances may be routed to one or more of the plurality of replicated instances for processing according to a data pre-processing operation.Type: GrantFiled: November 19, 2018Date of Patent: January 31, 2023Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Zehra Sura, Parijat Dube, Bishwaranjan Bhattacharjee, Tong Chen
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Patent number: 11551145Abstract: Systems, computer-implemented methods, and computer program products that can facilitate switching a model training process from a ground truth training phase to an adversarial training phase based on performance of a model trained in the ground truth training phase 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 analysis component that identifies a performance condition of a model trained in a model training process. The computer executable components can further comprise a trainer component that switches the model training process from a ground truth training process to an adversarial training process based on the identified performance condition.Type: GrantFiled: February 5, 2020Date of Patent: January 10, 2023Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Sidharth Gupta, Parijat Dube, Ashish Verma
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Publication number: 20220051142Abstract: Techniques for estimating runtimes of one or more machine learning tasks are provided. For example, one or more embodiments described herein can regard a system that can comprise a memory that stores 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 an extraction component that can extract a parameter from a machine learning task. The parameter can define a performance characteristic of the machine learning task. Also, the computer executable components can comprise a model component that can generate a model based on the parameter. Further, the computer executable components can comprise an estimation component that can generate an estimated runtime of the machine learning task based on the model.Type: ApplicationFiled: October 28, 2021Publication date: February 17, 2022Inventors: Parijat Dube, Gauri Joshi, Priya Ashok Nagpurkar, Stefania Costache, Diana Jeanne Arroyo, Zehra Noman Sura
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Patent number: 11200512Abstract: Techniques for estimating runtimes of one or more machine learning tasks are provided. For example, one or more embodiments described herein can regard a system that can comprise a memory that stores 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 an extraction component that can extract a parameter from a machine learning task. The parameter can define a performance characteristic of the machine learning task. Also, the computer executable components can comprise a model component that can generate a model based on the parameter. Further, the computer executable components can comprise an estimation component that can generate an estimated runtime of the machine learning task based on the model.Type: GrantFiled: February 21, 2018Date of Patent: December 14, 2021Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Parijat Dube, Gauri Joshi, Priya Ashok Nagpurkar, Stefania Costache, Diana Jeanne Arroyo, Zehra Noman Sura
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Patent number: 11182689Abstract: A method for performing machine learning includes assigning processing jobs to a plurality of model learners, using a central parameter server. The processing jobs includes solving gradients based on a current set of parameters. As the results from the processing job are returned, the set of parameters is iterated. A degree of staleness of the solving of the second gradient is determined based on a difference between the set of parameters when the jobs are assigned and the set of parameters when the jobs are returned. The learning rates used to iterate the parameters based on the solved gradients are proportional to the determined degrees of staleness.Type: GrantFiled: March 28, 2018Date of Patent: November 23, 2021Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Parijat Dube, Sanghamitra Dutta, Gauri Joshi, Priya A. Nagpurkar
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Patent number: 11151410Abstract: A computer-implemented method for data labeling is provided. The computer-implemented method assigns pseudo-labels to unlabeled examples of data using a similarity metric on an embedding space to produce pseudo-labeled examples. A curriculum learning model is trained using the pseudo-labeled examples. The curriculum learning model trained with the pseudo-labeled examples is employed in in a fine-tuning task to enhance classification accuracy of the data.Type: GrantFiled: September 7, 2018Date of Patent: October 19, 2021Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Patrick Watson, Bishwaranjan Bhattacharjee, Siyu Huo, Noel C. Codella, Brian M. Belgodere, Parijat Dube, Michael R. Glass, John R. Kender, Matthew L. Hill
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Publication number: 20210241169Abstract: Systems, computer-implemented methods, and computer program products that can facilitate switching a model training process from a ground truth training phase to an adversarial training phase based on performance of a model trained in the ground truth training phase 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 analysis component that identifies a performance condition of a model trained in a model training process. The computer executable components can further comprise a trainer component that switches the model training process from a ground truth training process to an adversarial training process based on the identified performance condition.Type: ApplicationFiled: February 5, 2020Publication date: August 5, 2021Inventors: Sidharth Gupta, Parijat Dube, Ashish Verma
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Publication number: 20210174191Abstract: In an embodiment, a method for fine-tuning a pre-trained neural network for transfer learning, the method comprising obtaining a first target feature vector from a first layer of a pre-trained neural network responsive to a first target data element of a target dataset passing therethrough, obtaining a first source feature vector associated with the first layer of the pre-trained neural network, calculating a first divergence value for the first layer of the pre-trained neural network based at least in part on the first target feature vector and the first source feature vector, and setting a learning rate for the first layer of the pre-trained neural network based at least in part on the first divergence value.Type: ApplicationFiled: December 5, 2019Publication date: June 10, 2021Applicant: International Business Machines CorporationInventors: Parijat Dube, Bishwaranjan Bhattacharjee, Patrick Watson, John Ronald Kender
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Patent number: 10929805Abstract: A method and system of simulating a cost of shipment are provided. A request for quote (RFQ) is received by a computing device from a shipper having a plurality of trade lanes. A revenue generated from each trade lane is estimated and ranked based on the estimated revenue. An original time limit is assigned to each trade lane. A trade lane with a highest ranking that has not yet been selected is selected. For the selected trade lane, graphs for space and time granularity analysis are generated. Space and time granularities that maximize accuracy within the assigned time limit based on the generated graphs are calculated. Cost simulation analysis is performed using the calculated space and time granularities. Upon determining that there are trade lanes not yet selected, there is a return to the act of selecting a trade lane.Type: GrantFiled: August 1, 2018Date of Patent: February 23, 2021Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Joao Goncalves, Parijat Dube
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Publication number: 20200349413Abstract: A neural network models fragmenting method, system, and computer program product include recursively factoring out common prefixes of models, constructing a hierarchy of decomposed model fragments based on the factoring, and grouping the constructed hierarchy for deployment.Type: ApplicationFiled: April 30, 2019Publication date: November 5, 2020Inventors: Vinod Muthusamy, Parijat Dube, Kaoutar El Maghraoui, Falk Pollok
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Publication number: 20200320379Abstract: Whether to train a new neural network model can be determined based on similarity estimates between a sample data set and a plurality of source data sets associated with a plurality of prior-trained neural network models. A cluster among the plurality of prior-trained neural network models can be determined. A set of training data based on the cluster can be determined. The new neural network model can be trained based on the set of training data.Type: ApplicationFiled: April 2, 2019Publication date: October 8, 2020Inventors: Patrick Watson, Bishwaranjan Bhattacharjee, Siyu Huo, Noel Christopher Codella, Brian Michael Belgodere, Parijat Dube, Michael Robert Glass, John Ronald Kender, Matthew Leon Hill
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Publication number: 20200160169Abstract: Embodiments for implementing mixed precision learning for neural networks by a processor. A neural network may be replicated into a plurality of replicated instances and each of the plurality of replicated instances differ in precision used for representing and determining parameters of the neural network. Data instances may be routed to one or more of the plurality of replicated instances for processing according to a data pre-processing operation.Type: ApplicationFiled: November 19, 2018Publication date: May 21, 2020Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Zehra SURA, Parijat DUBE, Bishwaranjan BHATTACHARJEE, Tong CHEN
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Publication number: 20200082210Abstract: A computer-implemented method for data labeling is provided. The computer-implemented method assigns pseudo-labels to unlabeled examples of data using a similarity metric on an embedding space to produce pseudo-labeled examples. A curriculum learning model is trained using the pseudo-labeled examples. The curriculum learning model trained with the pseudo-labeled examples is employed in in a fine-tuning task to enhance classification accuracy of the data.Type: ApplicationFiled: September 7, 2018Publication date: March 12, 2020Inventors: Patrick Watson, Bishwaranjan Bhattacharjee, Siyu Huo, Noel C. Codella, Brian M. Belgodere, Parijat Dube, Michael R. Glass, John R. Kender, Matthew L. Hill
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Publication number: 20200042938Abstract: A method and system of simulating a cost of shipment are provided. A request for quote (RFQ) is received by a computing device from a shipper having a plurality of trade lanes. A revenue generated from each trade lane is estimated and ranked based on the estimated revenue. An original time limit is assigned to each trade lane. A trade lane with a highest ranking that has not yet been selected is selected. For the selected trade lane, graphs for space and time granularity analysis are generated. Space and time granularities that maximize accuracy within the assigned time limit based on the generated graphs are calculated. Cost simulation analysis is performed using the calculated space and time granularities. Upon determining that there are trade lanes not yet selected, there is a return to the act of selecting a trade lane.Type: ApplicationFiled: August 1, 2018Publication date: February 6, 2020Inventors: Joao P. Goncalves, Parijat Dube
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Publication number: 20190354850Abstract: Techniques regarding autonomously facilitating the selection of one or more transfer models to enhance the performance of one or more machine learning tasks 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 an assessment component that can assess a similarity metric between a source data set and a sample data set from a target machine learning task. The computer executable components can also comprise an identification component that can identify a pre-trained neural network model associated with the source data set based on the similarity metric to perform the target machine learning task.Type: ApplicationFiled: May 17, 2018Publication date: November 21, 2019Inventors: Patrick Watson, Bishwaranjan Bhattacharjee, Noel Christopher Codella, Brian Michael Belgodere, Parijat Dube, Michael Robert Glass, John Ronald Kender, Siyu Huo, Matthew Leon Hill
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Publication number: 20190303787Abstract: A method for performing machine learning includes assigning processing jobs to a plurality of model learners, using a central parameter server. The processing jobs includes solving gradients based on a current set of parameters. As the results from the processing job are returned, the set of parameters is iterated. A degree of staleness of the solving of the second gradient is determined based on a difference between the set of parameters when the jobs are assigned and the set of parameters when the jobs are returned. The learning rates used to iterate the parameters based on the solved gradients are proportional to the determined degrees of staleness.Type: ApplicationFiled: March 28, 2018Publication date: October 3, 2019Inventors: PARIJAT DUBE, Sanghamitra Dutta, Gauri Joshi, Priya A. Nagpurkar