This AI Paper from NYU and Meta Reveals ‘Machine Learning Beyond Boundaries – How Fine-Tuning with High Dropout Rates Outshines Ensemble and Weight Averaging Methods’

In recent years, machine learning has significantly shifted away from the assumption that training and testing data come from the same distribution. Researchers have identified that models perform better when handling data from multiple distributions. This adaptability is often achieved through what’s known as “rich representations,” which exceed the capabilities of models trained under traditional […]

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