论文标题
偏离非因果机学习的偏离
Deviation bound for non-causal machine learning
论文作者
论文摘要
浓度不平等广泛用于分析机器学习算法。但是，目前的集中不平等不能应用于一些最受欢迎的深度神经网络，尤其是在自然语言处理中。这主要是由于每个数据点都取决于其他邻居数据点的意义，这主要是由于此类涉及数据的非混合性质。在本文中，提供了一个建模非毒物随机场的框架，并为此框架获得了Hoeffding型浓度不平等。该结果的证明依赖于非毒物随机场的局部近似，该函数的函数是有限的。随机变量。
Concentration inequalities are widely used for analyzing machine learning algorithms. However, current concentration inequalities cannot be applied to some of the most popular deep neural networks, notably in natural language processing. This is mostly due to the non-causal nature of such involved data, in the sense that each data point depends on other neighbor data points. In this paper, a framework for modeling non-causal random fields is provided and a Hoeffding-type concentration inequality is obtained for this framework. The proof of this result relies on a local approximation of the non-causal random field by a function of a finite number of i.i.d. random variables.
