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SYSTEM AND METHOD FOR MODEL-AGNOSTIC META-LEARNER FOR NOISY DATA WITH LABEL ERRORS
SYSTEM AND METHOD FOR MODEL-AGNOSTIC META-LEARNER FOR NOISY DATA WITH LABEL ERRORS
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机译:用于标签错误的嘈杂数据模型 - 不可知的Meta-Learner的系统和方法
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摘要
The present teaching relates to method, system, medium, and implementations for machine learning. Machine learning is performed based on training data via a dual loop learning process that includes a first loop for data decoding learning and a second loop for label decoding learning. In the first loop, first parameters associated with decoding are updated to generate updated first parameters based on a first label, estimated via the decoding using the first parameters, and a second label, predicted via the label decoding using second parameters. In the second loop, the second parameters associated with the label decoding are updated to generate updated second parameters based on a third label, obtained via the decoding using the updated first parameters, and a ground truth label.
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