论文标题
关于深度学习算法的隐式偏见
On the Implicit Bias in Deep-Learning Algorithms
论文作者
论文摘要
基于梯度的深度学习算法在实践中表现出色，但尽管有比训练示例更多的参数，但他们能够概括他们能够概括的原因并不理解。人们认为，隐性偏见是其概括能力的关键因素，因此近年来对其进行了广泛的研究。在这项简短的调查中，我们解释了隐性偏见的概念，回顾主要结果并讨论其含义。
Gradient-based deep-learning algorithms exhibit remarkable performance in practice, but it is not well-understood why they are able to generalize despite having more parameters than training examples. It is believed that implicit bias is a key factor in their ability to generalize, and hence it was widely studied in recent years. In this short survey, we explain the notion of implicit bias, review main results and discuss their implications.
