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CMU Develops Algorithm for Guaranteeing AI Model Generalization

June 1, 2021

Via: InfoQ

Researchers at Carnegie Mellon University’s (CMU) Approximately Correct Machine Intelligence (ACMI) Lab have published a paper on Randomly Assign, Train and Track (RATT), an algorithm that uses noisy training data to provide an upper bound on the true error risk of a deep-learning model. Using RATT, model developers can determine how well a model will generalize to new input data.

In the paper, which has been submitted to the upcoming International Conference on Machine Learning (ICML), the researchers show mathematical proofs of RATT’s guarantees and perform experiments on several benchmark datasets for natural language processing (NLP) and computer vision (CV) models.

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