Patents by Inventor Nikolaos KARIANAKIS
Nikolaos KARIANAKIS 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: 11960574Abstract: A method of balancing a dataset for a machine learning model includes identifying confusing classes of few-shot classes for a machine learning model during validation. One of the confusing classes and an image from one of the few-shot classes are selected. An image perturbation is computed such that the selected image is classified as the selected confusing class. The selected image is modified with the computed perturbation. The modified selected image is added to a batch for training the machine learning model.Type: GrantFiled: June 28, 2021Date of Patent: April 16, 2024Assignee: MICROSOFT TECHNOLOGY LICENSING, LLCInventors: Gaurav Mittal, Nikolaos Karianakis, Victor Manuel Fragoso Rojas, Mei Chen, Jedrzej Jakub Kozerawski
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Patent number: 11860975Abstract: Provided are aspects relating to methods and computing devices for allocating computing resources and selecting hyperparameter configurations during continuous retraining and operation of a machine learning model. In one example, a computing device configured to be located at a network edge between a local network and a cloud service includes a processor and a memory storing instructions executable by the processor to operate a machine learning model. During a retraining window, a selected portion of a video stream is selected for labeling. At least a portion of a labeled retraining data set is selected for profiling a superset of hyperparameter configurations. For each configuration of the superset of hyperparameter configurations, a profiling test is performed. The profiling test is terminated, and a change in inference accuracy that resulted from the profiling test is extrapolated. Based upon the extrapolated inference accuracies, a set of selected hyperparameter configurations is output.Type: GrantFiled: September 20, 2022Date of Patent: January 2, 2024Assignee: Microsoft Technology Licensing, LLCInventors: Ganesh Ananthanarayanan, Yuanchao Shu, Tsu-wang Hsieh, Nikolaos Karianakis, Paramvir Bahl, Romil Bhardwaj
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Patent number: 11704907Abstract: An object re-identifier. For each of a plurality of frames of a video, a quality of the frame is assessed and a confidence that a previously-recognized object is present in the frame is determined. The determined confidence for the frame is weighted based on the assessed quality of the frame such that frames with higher relative quality are weighted more heavily than frames with lower relative quality. An overall confidence that the previously-recognized object is present in the video is assessed based on the weighted determined confidences.Type: GrantFiled: December 17, 2021Date of Patent: July 18, 2023Assignee: Microsoft Technology Licensing, LLCInventors: Nikolaos Karianakis, Zicheng Liu, Yinpeng Chen
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Publication number: 20230030499Abstract: Examples are disclosed that relate to methods and computing devices for allocating computing resources and selecting hyperparameter configurations during continuous retraining and operation of a machine learning model. In one example, a computing device configured to be located at a network edge between a local network and a cloud service comprises a processor and a memory storing instructions executable by the processor to operate a machine learning model. During a retraining window, a selected portion of a video stream is selected for labeling. At least a portion of a labeled retraining data set is selected for profiling a superset of hyperparameter configurations. For each configuration of the superset of hyperparameter configurations, a profiling test is performed. The profiling test is terminated, and a change in inference accuracy that resulted from the profiling test is extrapolated. Based upon the extrapolated inference accuracies, a set of selected hyperparameter configurations is output.Type: ApplicationFiled: September 20, 2022Publication date: February 2, 2023Applicant: Microsoft Technology Licensing, LLCInventors: Ganesh ANANTHANARAYANAN, Yuanchao SHU, Tsu-wang HSIEH, Nikolaos KARIANAKIS, Paramvir BAHL, Romil BHARDWAJ
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Patent number: 11544561Abstract: Providing a task-aware recommendation of hyperparameter configurations for a neural network architecture. First, a joint space of tasks and hyperparameter configurations are constructed using a plurality of tasks (each of which corresponds to a dataset) and a plurality of hyperparameter configurations. The joint space is used as training data to train and optimize a performance prediction network, such that for a given unseen task corresponding to one of the plurality of tasks and a given hyperparameter configuration corresponding to one of the plurality of hyperparameter configurations, the performance prediction network is configured to predict performance that is to be achieved for the unseen task using the hyperparameter configuration.Type: GrantFiled: May 15, 2020Date of Patent: January 3, 2023Assignee: Microsoft Technology Licensing, LLCInventors: Gaurav Mittal, Victor Manuel Fragoso Rojas, Nikolaos Karianakis, Mei Chen, Chang Liu
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Publication number: 20220414392Abstract: A method of balancing a dataset for a machine learning model includes identifying confusing classes of few-shot classes for a machine learning model during validation. One of the confusing classes and an image from one of the few-shot classes are selected. An image perturbation is computed such that the selected image is classified as the selected confusing class. The selected image is modified with the computed perturbation. The modified selected image is added to a batch for training the machine learning model.Type: ApplicationFiled: June 28, 2021Publication date: December 29, 2022Inventors: Gaurav MITTAL, Nikolaos KARIANAKIS, Victor Manuel FRAGOSO ROJAS, Mei CHEN, Jedrzej Jakub KOZERAWSKI
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Publication number: 20220383188Abstract: Systems and methods are provided for merging models for use in an edge server under the multi-access edge computing environment. In particular, a model merger selects a layer of a model based on a level of memory consumption in the edge server and determines sharable layers based on common properties of the selected layer. The model merger generates a merged model by generating a single instantiation of a layer that corresponds to the sharable layers. A model trainer trains the merged model based on training data for the respective models to attain a level of accuracy of data analytics above a predetermined threshold. The disclosed technology further refreshes the merged model upon observing a level of data drift that exceeds a predetermined threshold. The refreshing of the merged model includes detaching and/or splitting consolidated sharable layers of sub-models in the merged model.Type: ApplicationFiled: September 10, 2021Publication date: December 1, 2022Applicant: Microsoft Technology Licensing, LLCInventors: Ganesh ANANTHANARAYANAN, Anand PADMANABHA IYER, Yuanchao SHU, Nikolaos KARIANAKIS, Arthi Hema PADMANABHAN
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Patent number: 11461591Abstract: Methods and computing devices for allocating computing resources and selecting hyperparameter configurations during continuous retraining and operation of a machine learning model. In one example, a computing device configured to be located at a network edge between a local network and a cloud service includes a processor and a memory storing instructions executable by the processor to operate a machine learning model. During a retraining window, a selected portion of a video stream is selected for labeling. At least a portion of a labeled retraining data set is selected for profiling a superset of hyperparameter configurations. For each configuration of the superset of hyperparameter configurations, a profiling test is performed. The profiling test is terminated, and a change in inference accuracy that resulted from the profiling test is extrapolated. Based upon the extrapolated inference accuracies, a set of selected hyperparameter configurations is output.Type: GrantFiled: December 16, 2020Date of Patent: October 4, 2022Assignee: Microsoft Technology Licensing, LLCInventors: Ganesh Ananthanarayanan, Yuanchao Shu, Tsu-wang Hsieh, Nikolaos Karianakis, Paramvir Bahl, Romil Bhardwaj
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Publication number: 20220188569Abstract: Examples are disclosed that relate to methods and computing devices for allocating computing resources and selecting hyperparameter configurations during continuous retraining and operation of a machine learning model. In one example, a computing device configured to be located at a network edge between a local network and a cloud service comprises a processor and a memory storing instructions executable by the processor to operate a machine learning model. During a retraining window, a selected portion of a video stream is selected for labeling. At least a portion of a labeled retraining data set is selected for profiling a superset of hyperparameter configurations. For each configuration of the superset of hyperparameter configurations, a profiling test is performed. The profiling test is terminated, and a change in inference accuracy that resulted from the profiling test is extrapolated. Based upon the extrapolated inference accuracies, a set of selected hyperparameter configurations is output.Type: ApplicationFiled: December 16, 2020Publication date: June 16, 2022Applicant: Microsoft Technology Licensing, LLCInventors: Ganesh ANANTHANARAYANAN, Yuanchao SHU, Tsu-wang HSIEH, Nikolaos KARIANAKIS, Paramvir BAHL, Romil BHARDWAJ
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Publication number: 20220172450Abstract: An object re-identifier. For each of a plurality of frames of a video, a quality of the frame is assessed and a confidence that a previously-recognized object is present in the frame is determined. The determined confidence for the frame is weighted based on the assessed quality of the frame such that frames with higher relative quality are weighted more heavily than frames with lower relative quality. An overall confidence that the previously-recognized object is present in the video is assessed based on the weighted determined confidences.Type: ApplicationFiled: December 17, 2021Publication date: June 2, 2022Applicant: Microsoft Technology Licensing, LLCInventors: Nikolaos KARIANAKIS, Zicheng LIU, Yinpeng CHEN
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Patent number: 11238300Abstract: An object re-identifier. For each of a plurality of frames of a video, a quality of the frame is assessed and a confidence that a previously-recognized object is present in the frame is determined. The determined confidence for the frame is weighted based on the assessed quality of the frame such that frames with higher relative quality are weighted more heavily than frames with lower relative quality. An overall confidence that the previously-recognized object is present in the video is assessed based on the weighted determined confidences.Type: GrantFiled: November 19, 2019Date of Patent: February 1, 2022Assignee: Microsoft Technology Licensing, LLCInventors: Nikolaos Karianakis, Zicheng Liu, Yinpeng Chen
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Publication number: 20210357744Abstract: Providing a task-aware recommendation of hyperparameter configurations for a neural network architecture. First, a joint space of tasks and hyperparameter configurations are constructed using a plurality of tasks (each of which corresponds to a dataset) and a plurality of hyperparameter configurations. The joint space is used as training data to train and optimize a performance prediction network, such that for a given unseen task corresponding to one of the plurality of tasks and a given hyperparameter configuration corresponding to one of the plurality of hyperparameter configurations, the performance prediction network is configured to predict performance that is to be achieved for the unseen task using the hyperparameter configuration.Type: ApplicationFiled: May 15, 2020Publication date: November 18, 2021Inventors: Gaurav MITTAL, Victor Manuel FRAGOSO ROJAS, Nikolaos KARIANAKIS, Mei CHEN, Chang LIU
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Publication number: 20210073563Abstract: An object re-identifier. For each of a plurality of frames of a video, a quality of the frame is assessed and a confidence that a previously-recognized object is present in the frame is determined. The determined confidence for the frame is weighted based on the assessed quality of the frame such that frames with higher relative quality are weighted more heavily than frames with lower relative quality. An overall confidence that the previously-recognized object is present in the video is assessed based on the weighted determined confidences.Type: ApplicationFiled: November 19, 2019Publication date: March 11, 2021Applicant: Microsoft Technology Licensing, LLCInventors: Nikolaos KARIANAKIS, Zicheng LIU, Yinpeng CHEN