Patents by Inventor Ala Eddine Ayadi

Ala Eddine Ayadi 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).

  • Optimizing training data for image classification
    Patent number: 11954572
    Abstract: A method for machine learning-based classification may include training a machine learning model with a full training data set, the full training data set comprising a plurality of data points, to generate a first model state of the machine learning model, generating respective embeddings for the data points in the full training data set with the first model state of the machine learning model, applying a clustering algorithm to the respective embeddings to generate one or more clusters of the embeddings, identifying outlier embeddings from the one or more clusters of the embeddings, generating a reduced training data set comprising the full training data set less the data points associated with the outlier embeddings, training the machine learning model with the reduced training data set to a second model state, and applying the second model state to one or more data sets to classify the one or more data sets.
    Type: Grant
    Filed: May 16, 2023
    Date of Patent: April 9, 2024
    Assignee: Home Depot Product Authority, LLC
    Inventors: Matthew Hagen, Estelle Afshar, Huiming Qu, Ala Eddine Ayadi, Jiaqi Wang
  • OPTIMIZING TRAINING DATA FOR IMAGE CLASSIFICATION
    Publication number: 20240070554
    Abstract: A method for machine learning-based classification may include training a machine learning model with a full training data set, the full training data set comprising a plurality of data points, to generate a first model state of the machine learning model, generating respective embeddings for the data points in the full training data set with the first model state of the machine learning model, applying a clustering algorithm to the respective embeddings to generate one or more clusters of the embeddings, identifying outlier embeddings from the one or more clusters of the embeddings, generating a reduced training data set comprising the full training data set less the data points associated with the outlier embeddings, training the machine learning model with the reduced training data set to a second model state, and applying the second model state to one or more data sets to classify the one or more data sets.
    Type: Application
    Filed: May 16, 2023
    Publication date: February 29, 2024
    Inventors: Matthew Hagen, Estelle Afshar, Huiming Qu, Ala Eddine Ayadi, Jiaqi Wang
  • Optimizing training data for image classification
    Patent number: 11687841
    Abstract: A method for machine learning-based classification may include training a machine learning model with a full training data set, the full training data set comprising a plurality of data points, to generate a first model state of the machine learning model, generating respective embeddings for the data points in the full training data set with the first model state of the machine learning model, applying a clustering algorithm to the respective embeddings to generate one or more clusters of the embeddings, identifying outlier embeddings from the one or more clusters of the embeddings, generating a reduced training data set comprising the full training data set less the data points associated with the outlier embeddings, training the machine learning model with the reduced training data set to a second model state, and applying the second model state to one or more data sets to classify the one or more data sets.
    Type: Grant
    Filed: June 5, 2020
    Date of Patent: June 27, 2023
    Assignee: HOME DEPOT PRODUCT AUTHORITY, LLC
    Inventors: Matthew Hagen, Estelle Afshar, Huiming Qu, Ala Eddine Ayadi, Jiaqi Wang
  • OPTIMIZING TRAINING DATA FOR IMAGE CLASSIFICATION
    Publication number: 20200387755
    Abstract: A method for machine learning-based classification may include training a machine learning model with a full training data set, the full training data set comprising a plurality of data points, to generate a first model state of the machine learning model, generating respective embeddings for the data points in the full training data set with the first model state of the machine learning model, applying a clustering algorithm to the respective embeddings to generate one or more clusters of the embeddings, identifying outlier embeddings from the one or more clusters of the embeddings, generating a reduced training data set comprising the full training data set less the data points associated with the outlier embeddings, training the machine learning model with the reduced training data set to a second model state, and applying the second model state to one or more data sets to classify the one or more data sets.
    Type: Application
    Filed: June 5, 2020
    Publication date: December 10, 2020
    Inventors: Matthew Hagen, Estelle Afshar, Huiming Qu, Ala Eddine Ayadi, Jiaqi Wang