Accelerated machine learning
AssigneeInternational Business Machines Corporation
InventorsMartin WISTUBA, Tejaswini PEDAPATI
Life of this patent
Estimating the term…
Abstract
Various embodiments are provided for accelerating machine learning in a computing environment by one or more processors in a computing system. Selected data may be received for training machine learning pipelines. Each of the machine learning pipelines may be scored according to one or more learning curves while training on selected data. Completion of the training on the selected data may be permitted for those of the machine learning pipelines having a score greater than a selected threshold. The training on the selected data may be terminated, prior to completion, on those of the machine learning pipelines having a score less than a selected threshold.
Claims25
A method for automated evaluation of machine learning models in a computing environment by one or more processors comprising:
- automatically evaluating and determining a level of robustness of a machine learning model against adversarial whitebox attacks;
- and
- analyzing results from the adversarial attack and generating a modified machine learning model in response to the evaluating and determining.
A system for automated evaluation of machine learning models in a computing environment, comprising:
- one or more computers with executable instructions that when executed cause the system to:
- automatically evaluate and determine a level of robustness of a machine learning model against adversarial whitebox attacks;
- and
- analyzing results from the adversarial attack and generating a modified machine learning model in response to the evaluating and determining.
A computer program product for automated evaluation of machine learning models in a computing environment, the computer program product comprising:
- one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instruction comprising:
- program instructions to automatically evaluate and determine a level of robustness of a machine learning model against adversarial whitebox attacks;
- and
- analyze results from the adversarial attack and generate a modified machine learning model in response to the evaluating and determining.
A method for automated evaluation of machine learning models using machine learning operations in a computing environment by one or more processors comprising:
- receiving a machine learning model, a data set used for testing the machine learning model, one or more adversarial attack objectives, an attack threat model, and a selected number of hyperparameters;
- automatically evaluating and determining a level of robustness of the machine learning model against an adversarial whitebox attack and analyzing results from the adversarial attack;
- and
- generating a modified machine learning model in response to the evaluating and determining.
A method for accelerating machine learning in a computing environment by one or more processors comprising:
- automatically evaluating and determining a level of robustness of the machine learning model against an adversarial whitebox attack by applying a data set used for testing the machine learning model, one or more adversarial attack objectives, an attack threat model, and a selected number of hyperparameters;
- generating an evaluation summary based on evaluating and determining of the level of robustness of the machine learning model.
Dependencies are inferred from the claim text.