Patents by Inventor Muhammad Bilal Zafar

Muhammad Bilal Zafar 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).

  • Patent number: 11977836
    Abstract: A determination is made that an explanatory data set for a common set of predictions generated by a machine learning model for records containing text tokens is to be provided. Respective groups of related tokens are identified from the text attributes of the records, and record-level prediction influence scores are generated for the token groups. An aggregate prediction influence score is generated for at least some of the token groups from the record-level scores, and an explanatory data set based on the aggregate scores is presented.
    Type: Grant
    Filed: November 26, 2021
    Date of Patent: May 7, 2024
    Assignee: Amazon Technologies, Inc.
    Inventors: Cedric Philippe Archambeau, Sanjiv Ranjan Das, Michele Donini, Michaela Hardt, Tyler Stephen Hill, Krishnaram Kenthapadi, Pedro L Larroy, Xinyu Liu, Keerthan Harish Vasist, Pinar Altin Yilmaz, Muhammad Bilal Zafar
  • Publication number: 20230289628
    Abstract: Apparatus and computer-implemented method, including: presetting data points which include pairs of mutually assigned input and output of a Gaussian process; determining a positive semi-definite kernel matrix from inputs predetermined by the data points; determining an inverse of the kernel matrix depending on an estimation for an inverse of a 1-Lipschitz mapping of the kernel matrix; presetting an input for the Gaussian process; determining a prediction for an expected value of the Gaussian process, and/or a prediction for a variance of the Gaussian process; determining a probable output variable of a sensor and/or a control variable for a machine depending on at least one of the predictions.
    Type: Application
    Filed: March 9, 2022
    Publication date: September 14, 2023
    Inventors: Muhammad Bilal ZAFAR, Martin Schiegg
  • Patent number: 11675361
    Abstract: A computer-implemented method for training a machine learning system for generating driving profiles and/or driving routes of a vehicle including: a generator obtains first random vectors and generates first driving routes and associated first driving profiles related to the first random vectors, driving routes and respectively associated driving profiles recorded in driving mode are stored in a data base, second driving routes and respectively associated second driving profiles recorded in driving mode are selected from the database, a discriminator obtains first pairs made up of first generated driving routes and respectively associated first generated driving profiles and second pairs made up of second driving routes and respectively associated second driving profiles recorded in driving mode, the discriminator calculates outputs that characterize each pair, and a target function is optimized as a function of the outputs of the discriminator.
    Type: Grant
    Filed: April 9, 2020
    Date of Patent: June 13, 2023
    Assignee: ROBERT BOSCH GMBH
    Inventors: Martin Schiegg, Muhammad Bilal Zafar, Stefan Angermaier
  • Patent number: 11661067
    Abstract: A computer-implemented method for training a machine learning system to generate driving profiles of a vehicle. The method includes first travel routes are selected from a first database having travel routes, a generator of the machine learning system receives the first travel routes and generates first driving profiles for each of the first travel routes, travel routes and associated driving profiles determined during vehicle operation are stored in a second database, second travel routes and respective associated second driving profiles determined during vehicle operation are selected from the second database, a discriminator of the machine learning system receives pairs made up of one of the first travel routes with the respective associated first generated driving profile and pairs made up of second travel routes with the respective associated second driving profile determined during vehicle operation, as input variables.
    Type: Grant
    Filed: March 24, 2020
    Date of Patent: May 30, 2023
    Assignee: ROBERT BOSCH GMBH
    Inventors: Martin Schiegg, Muhammad Bilal Zafar, Stefan Angermaier
  • Publication number: 20220180249
    Abstract: To simulate automotive systems, a large number of synthetic data points characterising an aspect of the performance of a target automotive system are generator. In this way, for example, various future scenarios can be simulated and statistically evaluated. A computer-implemented method is provided for training a generative machine learning model, a computer-implemented method for generating synthetic data series using a generative machine learning model, and an associated apparatus. An associated computer program element and computer readable medium are also described.
    Type: Application
    Filed: December 1, 2021
    Publication date: June 9, 2022
    Inventors: Martin Schiegg, Muhammad Bilal Zafar
  • Publication number: 20220172099
    Abstract: Bias metrics may be captured at different stages for training a machine learning model. A training job may specify bias metrics to capture at multiple different stages of a machine learning pipeline for a feature of a training data set used to train a machine learning model. The training job may be executed and the bias metrics determined at the stages as specified in the training job. The bias metrics for the different stages may be stored.
    Type: Application
    Filed: November 27, 2020
    Publication date: June 2, 2022
    Applicant: Amazon Technologies, Inc.
    Inventors: Sanjiv Das, Michele Donini, Jason Lawrence Gelman, Kevin Haas, Tyler Stephen Hill, Krishnaram Kenthapadi, Pinar Altin Yilmaz, Muhammad Bilal Zafar, Pedro L Larroy
  • Publication number: 20220171991
    Abstract: Views may be generated for bias metrics or feature attribution captured in machine learning pipelines. A request to create a view of bias metrics or feature attribution may be received. The bias metrics or feature attribution may have been determined in a machine learning pipeline as part of executing a training job that specified the bias metrics or the feature attribution. A development application may access a data store that stores the bias metrics or the feature attribution determined in the machine learning pipeline. A view based on the bias metrics or feature attribution may be generated and provided.
    Type: Application
    Filed: November 27, 2020
    Publication date: June 2, 2022
    Applicant: Amazon Technologies, Inc.
    Inventors: Sanjiv Das, Michele Donini, Jason Lawrence Gelman, Kevin Haas, Tyler Stephen Hill, Krishnaram Kenthapadi, Pinar Altin Yilmaz, Muhammad Bilal Zafar, Pedro L Larroy
  • Publication number: 20220172101
    Abstract: Feature attribution may be captured as part of a machine learning pipeline. A training job may include a request to determine feature attribution as part of a machine learning pipeline that trains a machine learning model from a training data set. A reference data set for determining the feature attribution of the machine learning model may be identified. The feature attribution may be determined based on the reference data set. The feature attribution of the trained machine learning model may be stored.
    Type: Application
    Filed: November 27, 2020
    Publication date: June 2, 2022
    Applicant: Amazon Technologies, Inc.
    Inventors: Sanjiv Das, Michele Donini, Jason Lawrence Gelman, Kevin Haas, Tyler Stephen Hill, Krishnaram Kenthapadi, Pinar Altin Yilmaz, Muhammad Bilal Zafar, Pedro L Larroy
  • Publication number: 20220172004
    Abstract: Bias metrics and feature attribution may be monitored for a machine learning model. A request to enable monitoring for bias metrics or feature attribution may be received. Monitoring may be enabled to evaluate respective performance of inferences of a machine learning model according to the enabled bias metrics or feature attribution. If a divergence from reference data is detected, then a notification indicating the divergence may be sent.
    Type: Application
    Filed: November 27, 2020
    Publication date: June 2, 2022
    Applicant: Amazon Technologies, Inc.
    Inventors: Sanjiv Das, Michele Donini, Jason Lawrence Gelman, Kevin Haas, Tyler Stephen Hill, Krishnaram Kenthapadi, Pinar Altin Yilmaz, Muhammad Bilal Zafar, Pedro L. Larroy
  • Publication number: 20210241174
    Abstract: A machine learning system and method of operating a machine learning system for determining a time series, comprising providing an input for a first in particular generative model depending on a probabilistic variable, determining an output of the first model in response to the input for the first model, the output of the first model characterizing the time series. The first model comprises a first layer that is trained to map input for the first model determined depending on the probabilistic variable to output characterizing intermediate data, and a second layer that is trained to map the intermediate data to the time series depending on an output of a third layer of the first model. The output of the third layer characterizes a physical constraint to a machine state. Values of the time series or of the intermediate data are constrained by the output of the third layer.
    Type: Application
    Filed: December 30, 2020
    Publication date: August 5, 2021
    Inventors: Martin Schiegg, Muhammad Bilal Zafar
  • Publication number: 20210241104
    Abstract: A device, a machine learning system and a method for determining a velocity of a vehicle. The method includes providing an input for a first generative model depending on a route information, a probabilistic variable, including noise, and an output of a second physical model, determining an output of the first model in response to the input for the first model. The output of the first model characterizes the velocity. The first model comprises a first component that is trained to map input for the first model determined depending on the route information and the probabilistic variable to intermediate output for the velocity of the vehicle. The first model comprises a second component that is trained to map the intermediate output to the velocity depending on the output of the second model. The output of the second model characterizes a physical constraint for the velocity or for the intermediate output.
    Type: Application
    Filed: January 27, 2021
    Publication date: August 5, 2021
    Inventors: Martin Schiegg, Muhammad Bilal Zafar, Kai Sandmann
  • Publication number: 20210237745
    Abstract: A method for determining a state of a transmission for a vehicle, including providing an input for a first generative model depending on a route information, a vehicle speed, a probabilistic variable, and an output of a second physical model, and determining an output of the first model characterizing the state in response to the input for the first model. The first model comprises a first layer trained to map input to an intermediate state. The first model comprises a second layer trained to map the intermediate state to the state depending on the output of the second model. The method includes providing an input for the second physical model depending on at least one vehicle state and/or the route information, and determining an output of the second model in response to the input for the second model. The output of the second model characterizes limit(s) for the intermediate state.
    Type: Application
    Filed: January 22, 2021
    Publication date: August 5, 2021
    Inventors: Martin Schiegg, Muhammad Bilal Zafar, Roman Dominik Kilgus, Sebastian Gerwinn
  • Publication number: 20210209489
    Abstract: A system for processing a classifier. The classifier is a Naïve Bayes-type classifier classifying an input instance into multiple classes based on multiple continuous probability distributions of respective features of the input instance and based on prior probabilities of the multiple classes. Upon receiving a removal request message identifying one or more undesired training instances, the classifier is made independent from one or more undesired training instances. To this end, for a continuous probability distribution of a feature, adapted parameters of the probability distribution are computed based on current parameters of the probability distribution and the one or more undesired training instances. Further, an adapted prior probability of a class is computed based on a current prior probability of the class and the one or more undesired training instances.
    Type: Application
    Filed: January 5, 2021
    Publication date: July 8, 2021
    Inventors: Muhammad Bilal Zafar, Christoph Zimmer, Maja Rita Rudolph, Martin Schiegg, Sebastian Gerwinn
  • Publication number: 20210209507
    Abstract: A system for processing a model. The model provides a model output given an input instance. The model has been trained on a training dataset by iteratively optimizing an objective function including losses according to a loss function for training instances of the training dataset. Upon receiving a removal request message identifying one or more undesired training instances of the training dataset, the model is made independent from the one or more undesired training instances. To this end, the one or more undesired training instances are removed from the training dataset to obtain a remainder dataset, and an adapted model is determined for the remainder dataset. The parameters of the adapted model are first initialized based on the set of parameters of the trained model, and then iteratively adapted by optimizing the objective function with respect to the remainder dataset.
    Type: Application
    Filed: January 5, 2021
    Publication date: July 8, 2021
    Inventors: Muhammad Bilal Zafar, Christoph Zimmer, Maja Rita Rudolph, Martin Schiegg, Sebastian Gerwinn
  • Publication number: 20200333793
    Abstract: A computer-implemented method for training a machine learning system for generating driving profiles and/or driving routes of a vehicle including: a generator obtains first random vectors and generates first driving routes and associated first driving profiles related to the first random vectors, driving routes and respectively associated driving profiles recorded in driving mode are stored in a data base, second driving routes and respectively associated second driving profiles recorded in driving mode are selected from the database, a discriminator obtains first pairs made up of first generated driving routes and respectively associated first generated driving profiles and second pairs made up of second driving routes and respectively associated second driving profiles recorded in driving mode, the discriminator calculates outputs that characterize each pair, and a target function is optimized as a function of the outputs of the discriminator.
    Type: Application
    Filed: April 9, 2020
    Publication date: October 22, 2020
    Inventors: Martin Schiegg, Muhammad Bilal Zafar, Stefan Angermaier
  • Publication number: 20200331473
    Abstract: A computer-implemented method for training a machine learning system to generate driving profiles of a vehicle. The method includes first travel routes are selected from a first database having travel routes, a generator of the machine learning system receives the first travel routes and generates first driving profiles for each of the first travel routes, travel routes and associated driving profiles determined during vehicle operation are stored in a second database, second travel routes and respective associated second driving profiles determined during vehicle operation are selected from the second database, a discriminator of the machine learning system receives pairs made up of one of the first travel routes with the respective associated first generated driving profile and pairs made up of second travel routes with the respective associated second driving profile determined during vehicle operation, as input variables.
    Type: Application
    Filed: March 24, 2020
    Publication date: October 22, 2020
    Inventors: Martin Schiegg, Muhammad Bilal Zafar, Stefan Angermaier