Computer Science > Machine Learning
[Submitted on 25 Aug 2020 (v1), last revised 22 Dec 2020 (this version, v2)]
Title:Stochastic Markov Gradient Descent and Training Low-Bit Neural Networks
View PDFAbstract:The massive size of modern neural networks has motivated substantial recent interest in neural network quantization. We introduce Stochastic Markov Gradient Descent (SMGD), a discrete optimization method applicable to training quantized neural networks. The SMGD algorithm is designed for settings where memory is highly constrained during training. We provide theoretical guarantees of algorithm performance as well as encouraging numerical results.
Submission history
From: Jonathan Ashbrock [view email][v1] Tue, 25 Aug 2020 15:48:15 UTC (224 KB)
[v2] Tue, 22 Dec 2020 15:48:20 UTC (224 KB)
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