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ARTIFICIAL INTELLIGENT SYSTEMS AND METHODS FOR USING STRUCTURALLY SIMPLER LEARNER MODEL TO MIMIC BEHAVIORS OF STRUCTURALLY MORE COMPLICATED REFERENCE MODEL
ARTIFICIAL INTELLIGENT SYSTEMS AND METHODS FOR USING STRUCTURALLY SIMPLER LEARNER MODEL TO MIMIC BEHAVIORS OF STRUCTURALLY MORE COMPLICATED REFERENCE MODEL
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机译：将结构简化学习者模型用于结构更复杂的参考模型的模拟行为的人工智能系统和方法
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摘要
A method for using a structurally more complicated reference model to train a structurally simpler learner model includes: obtaining a trained reference model at least including N reference blocks and a learner model at least including N learner blocks respectively corresponding to the N reference blocks; training the learner model by conducting an iterative operation; determining whether the learner model is convergent; and in response to that the learner model is convergent, stopping the iterative operation to assign the learner model as a trained learner model. The iterative operation includes inputting a sample data set into the trained reference model and the learner model; for each of the N learner blocks: determining a distance between a learner vector of the learner block and a reference vector of the reference block, and updating parameters in the learner block based on the determined distance.
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