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[D] Many papers don’t do hyperparameter search on DNN baselines

A thing that I recognized after reading various DNN model papers is that they often don’t seem to perform hyperparameter search on their / baseline models. Many reported results seem to be for hand-picked configurations only. No search methods (like grid search, Bayesian optimization or even random search) have been used to find the best-performing configurations.

IMO this is a problem: The performance of a DNN models really depends on the choice of hyperparameters, so hypothetically you could make a baseline model perform badly by picking poor hyperparameters.

Why are so many big papers with such an incomplete evaluation out there? Or am I missing something here and it is enough to look at one configuration only?

submitted by /u/alex19111
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.