论文标题
展示在功能学习应用程序中使用自动编码器的展示
A Showcase of the Use of Autoencoders in Feature Learning Applications
论文作者
论文摘要
自动编码器是基于人工神经网络的数据表示学习的技术。与其他特征学习方法的不同，可能着重于查找特征空间的特定转换的其他特征学习方法，它们可以适应以实现许多目的，例如数据可视化，去核，异常检测和语义散发。这项工作介绍了这些应用程序，并提供了有关自动编码器如何执行它们的详细信息，包括使用带有易于使用的自动编码器设计和训练的R软件包的代码样本，\ Textttt {Ruta}。一路上，提供了如何完成每个学习任务的解释，目的是帮助读者为这些或其他目标设计自己的自动编码器。
Autoencoders are techniques for data representation learning based on artificial neural networks. Differently to other feature learning methods which may be focused on finding specific transformations of the feature space, they can be adapted to fulfill many purposes, such as data visualization, denoising, anomaly detection and semantic hashing. This work presents these applications and provides details on how autoencoders can perform them, including code samples making use of an R package with an easy-to-use interface for autoencoder design and training, \texttt{ruta}. Along the way, the explanations on how each learning task has been achieved are provided with the aim to help the reader design their own autoencoders for these or other objectives.
