Improving the Backpropagation Algorithm with Consequentialism Weight Updates over Mini-Batches
This work addresses the training efficiency of deep neural networks, but it appears incremental as it builds on existing adaptive filter methods.
The authors tackled the problem of improving backpropagation by introducing a new algorithm that predicts and corrects adverse consequences before they occur, showing its usefulness in training deep neural networks.
Many attempts took place to improve the adaptive filters that can also be useful to improve backpropagation (BP). Normalized least mean squares (NLMS) is one of the most successful algorithms derived from Least mean squares (LMS). However, its extension to multi-layer neural networks has not happened before. Here, we first show that it is possible to consider a multi-layer neural network as a stack of adaptive filters. Additionally, we introduce more comprehensible interpretations of NLMS than the complicated geometric interpretation in affine projection algorithm (APA) for a single fully-connected (FC) layer that can easily be generalized to, for instance, convolutional neural networks and also works better with mini-batch training. With this new viewpoint, we introduce a better algorithm by predicting then emending the adverse consequences of the actions that take place in BP even before they happen. Finally, the proposed method is compatible with stochastic gradient descent (SGD) and applicable to momentum-based derivatives such as RMSProp, Adam, and NAG. Our experiments show the usefulness of our algorithm in the training of deep neural networks.