论文标题
辅助网络：动态系统的可扩展且敏捷的在线学习，具有不一致的输入
Auxiliary Network: Scalable and agile online learning for dynamic system with inconsistently available inputs
论文作者
论文摘要
流分类方法假设输入功能的数量已固定并始终接收。但是，在许多实际情况下，需求是一些输入功能是可靠的，而另一些则不可靠或不一致。在本文中，我们提出了一个新型的基于深度学习的模型，称为辅助网络（AUX-NET），该模型可扩展且敏捷。它采用加权分类器的合奏来提供最终结果。 AUX-NET模型基于对冲算法和在线梯度下降。它使用单个通行证学习在线设置中采用了不同深度的模型。 AUX-NET是针对可扩展神经网络模型的基础工作，用于需要临时或不一致的输入数据的动态复杂环境。 AUX-NET的功效显示在公共数据集中。
Streaming classification methods assume the number of input features is fixed and always received. But in many real-world scenarios demand is some input features are reliable while others are unreliable or inconsistent. In this paper, we propose a novel deep learning-based model called Auxiliary Network (Aux-Net), which is scalable and agile. It employs a weighted ensemble of classifiers to give a final outcome. The Aux-Net model is based on the hedging algorithm and online gradient descent. It employs a model of varying depth in an online setting using single pass learning. Aux-Net is a foundational work towards scalable neural network model for a dynamic complex environment requiring ad hoc or inconsistent input data. The efficacy of Aux-Net is shown on public dataset.
