论文标题
生物学上合理的学习规则是否需要跳过连接？
Are skip connections necessary for biologically plausible learning rules?
论文作者
论文摘要
反向传播是深度学习的主力，但是，还引入了其他几种生物学动机的学习规则，例如随机反馈对准和差异目标传播。这些方法都没有产生反向传播的竞争性能。在本文中，我们表明，在中间层之间具有跳过连接的生物动机学习规则可以执行MNIST数据集上的反向传播，并且对各种超级参数组都有鲁棒性。
Backpropagation is the workhorse of deep learning, however, several other biologically-motivated learning rules have been introduced, such as random feedback alignment and difference target propagation. None of these methods have produced a competitive performance against backpropagation. In this paper, we show that biologically-motivated learning rules with skip connections between intermediate layers can perform as well as backpropagation on the MNIST dataset and are robust to various sets of hyper-parameters.
