US Patent Application 18228645. SEMANTICS PRESERVATION FOR MACHINE LEARNING MODELS DEPLOYED AS DEPENDENT ON OTHER MACHINE LEARNING MODELS simplified abstract
SEMANTICS PRESERVATION FOR MACHINE LEARNING MODELS DEPLOYED AS DEPENDENT ON OTHER MACHINE LEARNING MODELS
Organization Name
Inventor(s)
Edouard Godfrey of Sunnyvale CA (US)
Gianpaolo Fasoli of Redwood City CA (US)
Kuangyu Wang of Cupertino CA (US)
SEMANTICS PRESERVATION FOR MACHINE LEARNING MODELS DEPLOYED AS DEPENDENT ON OTHER MACHINE LEARNING MODELS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18228645 titled 'SEMANTICS PRESERVATION FOR MACHINE LEARNING MODELS DEPLOYED AS DEPENDENT ON OTHER MACHINE LEARNING MODELS
Simplified Explanation
- The subject technology involves using machine learning models to classify input data. - The first machine learning model on a client electronic device determines assessment values, which represent the classifications of the input data. - These assessment values are associated with constraint data, which is a probability distribution of the assessment values based on the classifications of the input data. - The assessment values determined by the first model are then applied to a second machine learning model to determine the classifications of the input data. - The subject technology checks if the accuracies of the classifications determined by the second model align with the probability distribution from the first model. - If the accuracies do not conform with the probability distribution, the first machine learning model is retrained.
Original Abstract Submitted
The subject technology receives assessment values determined by a first machine learning model deployed on a client electronic device, the assessment values being indicative of classifications of input data and the assessment values being associated with constraint data that comprises a probability distribution of the assessment values with respect to the classifications of the input data. The subject technology applies the assessment values determined by the first machine learning model to a second machine learning model to determine the classifications of the input data. The subject technology determines whether accuracies of the classifications determined by the second machine learning model conform with the probability distribution for corresponding assessment values determined by the first machine learning model. The subject technology retrains the first machine learning model when the accuracies of the classifications determined by the second machine learning model do not conform with the probability distribution.
