# Smallify: Learning Network Size while Training

@article{Leclerc2018SmallifyLN, title={Smallify: Learning Network Size while Training}, author={Guillaume Leclerc and Manasi Vartak and Raul Castro Fernandez and Tim Kraska and Samuel Madden}, journal={ArXiv}, year={2018}, volume={abs/1806.03723} }

As neural networks become widely deployed in different applications and on different hardware, it has become increasingly important to optimize inference time and model size along with model accuracy. Most current techniques optimize model size, model accuracy and inference time in different stages, resulting in suboptimal results and computational inefficiency. In this work, we propose a new technique called Smallify that optimizes all three of these metrics at the same time. Specifically we…

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