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Fast Benchmarking of Accuracy vs. Training Time with Cyclic Learning Rates
Jacob Portes · Davis Blalock · Cory Stephenson · Jonathan Frankle
Event URL: https://openreview.net/forum?id=Uad23IcIEs »
Benchmarking the tradeoff between neural network accuracy and training time is computationally expensive. Here we show how a multiplicative cyclic learning rate schedule can be used to construct a tradeoff curve in a single training run. We generate cyclic tradeoff curves for combinations of training methods such as Blurpool, Channels Last, Label Smoothing and MixUp, and highlight how these cyclic tradeoff curves can be used to efficiently evaluate the effects of algorithmic choices on network training.
Author Information
Jacob Portes (MosaicML)
I am interested in developing experimentally motivated theories of both artificial and biological neural networks. I recently finished my PhD in the Center for Theoretical Neuroscience at Columbia University. I like it when machine learning informs neuroscience, and when neuroscience inspires machine learning. As of July 2022, I am a Research Scientist at MosaicML.
Davis Blalock (MIT)
Cory Stephenson (Intel)
Jonathan Frankle (MIT CSAIL)
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