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Sep, 2020
贝叶斯感知机: 迈向全贝叶斯神经网络
Bayesian Perceptron: Towards fully Bayesian Neural Networks
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Marco F. Huber
TL;DR
本文提出了一种全新的方法,即基于贝叶斯推断框架通过闭式计算对感知机进行训练和预测,其中感知机的权重和预测被视作高斯随机变量,为常用的激活函数,如sigmoid或ReLU提供了预测感知机输出和学习权重的解析表达式,该方法不需要计算昂贵的梯度计算,进一步允许顺序学习。
Abstract
artificial neural networks
(NNs) have become the de facto standard in
machine learning
. They allow learning highly nonlinear transformations in a plethora of applications. However, NNs usually only provide point
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