论文标题
类似大脑的方法无监督学习隐藏表示形式 - 一项比较研究
Brain-like approaches to unsupervised learning of hidden representations -- a comparative study
论文作者
论文摘要
近年来，对隐藏表示形式的无监督学习一直是机器学习中最活跃的研究方向之一。在这项工作中，我们研究了脑样贝叶斯置信度传播神经网络（BCPNN）模型，该模型最近扩展到提取稀疏的分布式高维表示。使用外部线性分类器研究了在MNIST和时尚范围数据集进行培训时，隐藏表示的有用性和依赖性的可分离性，并将其与包括受限的Boltzmann机器和自动装编码器的其他无监督学习方法进行了比较。
Unsupervised learning of hidden representations has been one of the most vibrant research directions in machine learning in recent years. In this work we study the brain-like Bayesian Confidence Propagating Neural Network (BCPNN) model, recently extended to extract sparse distributed high-dimensional representations. The usefulness and class-dependent separability of the hidden representations when trained on MNIST and Fashion-MNIST datasets is studied using an external linear classifier and compared with other unsupervised learning methods that include restricted Boltzmann machines and autoencoders.
