The Backpropagation Algorithm is the most popular training algorithm for the Multi-Layer Per ceptron, despite its notorious slowness. Part of this slowness may be attributed to a phenomenon of the training process that has been called the Herd Effect. This paper describes a modification of the ...
The backpropagation algorithm is based on common linear algebraic operations - things like vector addition, multiplying a vector by a matrix, and so on. But one of the operations is a little less commonly used. In particular, supposess andtt are two vectors of the same dimension. Then we u...
At this point, let me say that the paper notations are not the commonly used notations currently. However, I provide a simple dictionary to make the shift to common notations easier. There is already a good post about the paper’s backprop algorithm Learning Backpropagation from Geoff Hinton ...
In this paper we derive and describe in detail an efficient backpropagation algorithm (named BPFCC) for computing the gradient for FCC networks. Actually, the backpropagation in BPFCC is an elaborately designed process for computing the derivative amplification coefficients, which are essential for ...
The algorithm consists of five steps: (1) Each image is filtered at different orientations with bar masks of four sizes that increase with eccentricity; the equivalent filters are one or two octaves wide. (2) Zero-crossings in the filtered images, which roughly correspond to edges, are ...
Has Back-propagation algorithm, one of the most powerful artificial neural network techniques outlived its utility in the AI world? Find out more
We use the AdamW36 algorithm as the optimizer, the learning rate lr is set with 1 × 10-3, and the same learning rate control strategy as in SGDR37 is used. The same method in temporal spike sequence-learning backpropagation (TSSL-BP) is used to warm up the model. The membrane ...
The new graph is shown to be interreciprocal with the original and to correspond to the backpropagation-through-time algorithm. Interreciprocity provides a theoretical argument to verify that both flow graphs implement the same overall weight update. 展开 ...
The back-propagation algorithm can be thought of as a way of performing a supervised learning process by means of examples, using the following general approach: A problem, for example, a set of inputs, is presented to the network, and the response from the network is recorded. ...
The algorithm is shown to work better than the original back-propagation and is comparable with the Levenberg-Marquardt algorithm, but simpler and easier to implement comparing to Levenberg-Marquardt algorithm. 展开 关键词: backpropagation chaos feedforward neural nets minimisation training Wincson ...