论文标题
贝叶斯信心传播神经网络中的学习表征
Learning representations in Bayesian Confidence Propagation neural networks
论文作者
论文摘要
对分层表示的无监督学习一直是近年来深度学习中最有活力的研究方向之一。在这项工作中，我们研究了基于本地Hebbian学习的神经网络中无监督的策略。我们提出了新的机制，以扩展贝叶斯置信度传播神经网络（BCPNN）体系结构，并在MNIST数据集上进行测试时，证明了它们无监督学习显着隐藏表示的能力。
Unsupervised learning of hierarchical representations has been one of the most vibrant research directions in deep learning during recent years. In this work we study biologically inspired unsupervised strategies in neural networks based on local Hebbian learning. We propose new mechanisms to extend the Bayesian Confidence Propagating Neural Network (BCPNN) architecture, and demonstrate their capability for unsupervised learning of salient hidden representations when tested on the MNIST dataset.
