Abstract:Machine Learning (ML) has made its way into a wide variety of advanced applications, where high accuracies can be achieved when these ML models are evaluated in the same context as they were trained and validated on. However, when these high-accuracy models are exposed to out-of-distribution points such as noisy inputs, their performance could potentially degrade significantly. Recommending the most suitable ML model that retains a higher accuracy when exposed to these noisy inputs can overcome this performanc… Show more
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