论文标题
前进算法：一些初步调查
The Forward-Forward Algorithm: Some Preliminary Investigations
论文作者
论文摘要
本文的目的是为神经网络介绍新的学习程序，并证明它在一些小问题上效果很好，值得进一步研究。前向前向算法将反向传播的前向和向后传递替换为两个正向通行证，一个带有正（即真实）数据，另一个带有负面数据，该数据可以由网络本身生成。每一层都有其自己的目标函数，这仅仅是对正数据具有很高的优点，而负面数据的优点较低。一层平方活动的总和可以用作善良，但还有许多其他可能性，包括减去平方活动的总和。如果可以及时分开正面和负面的通行证，则可以离线进行负面通行证，这将使学习在积极通行证中变得更加简单，并允许视频通过网络管道而无需存储活动或停止以传播衍生品。
The aim of this paper is to introduce a new learning procedure for neural networks and to demonstrate that it works well enough on a few small problems to be worth further investigation. The Forward-Forward algorithm replaces the forward and backward passes of backpropagation by two forward passes, one with positive (i.e. real) data and the other with negative data which could be generated by the network itself. Each layer has its own objective function which is simply to have high goodness for positive data and low goodness for negative data. The sum of the squared activities in a layer can be used as the goodness but there are many other possibilities, including minus the sum of the squared activities. If the positive and negative passes could be separated in time, the negative passes could be done offline, which would make the learning much simpler in the positive pass and allow video to be pipelined through the network without ever storing activities or stopping to propagate derivatives.
