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A Boo(n) for Evaluating Architecture Performance
Ondrej Bajgar · Rudolf Kadlec · Jan Kleindienst

Fri Jul 13 09:15 AM -- 12:00 PM (PDT) @ Hall B #20
We point out important problems with the common practice of using the best single model performance for comparing deep learning architectures, and we propose a method that corrects these flaws. Each time a model is trained, one gets a different result due to random factors in the training process, which include random parameter initialization and random data shuffling. Reporting the best single model performance does not appropriately address this stochasticity. We propose a normalized expected best-out-of-$n$ performance ($\text{Boo}_n$) as a way to correct these problems.

Author Information

Ondrej Bajgar (IBM Watson)
Rudolf Kadlec (IBM Watson)
Jan Kleindienst (IBM Watson)

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