This paper demonstrates how the backpropagation algorithm (BP) and its variants can be accelerated significantly while the quality of the trained nets will increase. Two modifications were proposed: First, instead of the usual quadratic error we use the cross entropy as an error function and ...
This paper demonstrates how the backpropagation algorithm (BP) and its variants can be accelerated significantly while the quality of the trained nets will... M Joost,W Schiffmann - International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 被引量: 65发表: 1998年 An Improved Back...
In this paper, we revisit the recurrent back-propagation (RBP) algorithm, discuss the conditions under which it applies as well as how to satisfy them in deep neural networks. We show that RBP can be unstable and propose two variants based on conjugate gradient on the normal equations (CG-...
The capabilities of natural neural systems have inspired both new generations of machine learning algorithms as well as neuromorphic, very large-scale integrated circuits capable of fast, low-power information processing. However, it has been argued that
The capabilities of natural neural systems have inspired both new generations of machine learning algorithms as well as neuromorphic, very large-scale integrated circuits capable of fast, low-power information processing. However, it has been argued that
The backpropagation algorithm is a form of steepest-descent algorithm in which the error signal, which is the difference between the current output of the neural network and the desired output signal, is used to adjust the weights in the output layer, and is then used to adjust the weights ...
反向传播算法(Backpropagation Algorithm,简称BP算法)是深度学习的重要思想基础,对于初学者来说也是必须要掌握的基础知识!本文希望以一个清晰的脉络和详细的说明,来让读者彻底明白BP算法的原理和计算过程。本文主要由以下部分组成: 1. 前...backpropagation-反向传播 https://www.zybuluo.com/hanbingtao/note/476663...
The number of hidden layer neurons used is 10, and the activation function used is Sigmoid. Since weights are adjusted in the steepest descent direction, the backpropagation algorithm does not guarantee the fastest convergence. To avoid that, in this work, we used the scaled conjugate gradient ...
Overall, the most relevant attribute was the cost, so we decided to establish this as the criteria for selection of the rows and columns for sparsification. An explanation of the full process of this method is given in Algorithm 1. Algorithm 1 SLRProp 𝑊𝑒𝑖𝑔ℎ𝑡𝑠1←Weights1...
algorithm that is fast and accurate, like backprop, but much simpler as it avoids all transport of synaptic weight information. Our aim is to describe this novel algorithm and its potential relevance in as simple a form as possible, meaning that we overlook aspects of neurophysiology that will ...