[PDF] A Bayesian Optimization Framework for Neural Network Compression
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We propagate Gaussian uncertainties from the statistics through the Bayesian optimization framework yielding a method that gives a probabilistic approximation.
In this work, we develop a general Bayesian optimization framework for optimizing functions that are computed based on U-statistics. We propagate Gaussian ...
We propagate Gaussian uncertainties from the statistics through the Bayesian optimization framework yielding a method that gives a probabilistic approximation.
In this work, we develop a general Bayesian optimization framework for optimizing functions that are computed based on U-statistics. We propagate Gaussian ...
In this section, we show that compression with the L2 knowledge distillation objective can lead to a generalization bound on the performance of the compressed ...
Dec 1, 2020 · Bayesian Optimization (B.O.) is an optimization framework ... Model compression as constrained optimization, with application to neural nets.
In "Bayesian Compression for Deep Learning" we adopt a Bayesian view for the compression of neural networks. By revisiting the connection between the ...
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A systematic framework is proposed for the estimation of an optimal neural network structure using Bayesian optimization, denoted as the Bayesian-optimized ...
Jun 23, 2020 · In this paper we propose a compression principle that states that an optimal predictive model is the one that minimizes a total compressed message length of ...
A Bayesian Optimization Framework for Neural Network Compression · Xingchen Ma ... compress a pretrained large neural network into optimal smaller networks ...