activity
20182020
most citedReLeQ: A Reinforcement Learning Approach for Deep Quantization of Neural Networks

43 citations · 57 across the 5 of their papers we have counts for

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Showing 2019Show all

2 papers · 1 filter

cs.LG2019★ 6 cited

Divide and Conquer: Leveraging Intermediate Feature Representations for Quantized Training of Neural Networks

Ahmed T. Elthakeb, Prannoy Pilligundla, Alex Cloninger +1

The deep layers of modern neural networks extract a rather rich set of features as an input propagates through the network. This paper sets out to harvest these rich intermediate r…

cs.LG2019★ 5 cited

SinReQ: Generalized Sinusoidal Regularization for Low-Bitwidth Deep Quantized Training

Ahmed T. Elthakeb, Prannoy Pilligundla, Hadi Esmaeilzadeh

Deep quantization of neural networks (below eight bits) offers significant promise in reducing their compute and storage cost. Albeit alluring, without special techniques for train…