论文标题
字母的字母：发现自然功能集
Letters of the Alphabet: Discovering Natural Feature Sets
论文作者
论文摘要
深度学习网络使用反向传播算法在大数据集中找到复杂的功能。该算法反复调整网络连接。”权重并检查输入和输出层之间的“隐藏”节点行为，可以更好地了解神经网络如何创建特征表示。彼此建立的实验表明，在一层中计算的活动差异可以指导学习。使用了一个简单的神经网络，其中包括一个由字母字母组成的数据集，其中每个字母构成由0和1s组成的81个输入节点以及一个隐藏层和一个输出层。第一个实验说明了这个简单的神经网络中的隐藏层如何代表输入数据的特征。第二个实验试图逆转神经网络，以找到字母的自然特征集。当网络解释功能时，我们可以了解它如何为给定数据衍生自然功能集。这种理解对于深入研究深层生成模型（例如玻尔兹曼机器）至关重要。深度生成模型是一类无监督的深度学习算法。深生成模型的主要功能是找到给定数据集的自然特征集。
Deep learning networks find intricate features in large datasets using the backpropagation algorithm. This algorithm repeatedly adjusts the network connections.' weights and examining the "hidden" nodes behavior between the input and output layer provides better insight into how neural networks create feature representations. Experiments built on each other show that activity differences computed within a layer can guide learning. A simple neural network is used, which includes a data set comprised of the alphabet letters, where each letter forms 81 input nodes comprised of 0 and 1s and a single hidden layer and an output layer. The first experiment explains how the hidden layers in this simple neural network represent the input data's features. The second experiment attempts to reverse-engineer the neural network to find the alphabet's natural feature sets. As the network interprets features, we can understand how it derives the natural feature sets for a given data. This understanding is essential to delve deeper into deep generative models, such as Boltzmann machines. Deep generative models are a class of unsupervised deep learning algorithms. The primary function of deep generative models is to find the natural feature sets for a given data set.
