backpropagation algorithmhypercube architectureslearning algorithmmassively parallel computer architecture2D-grid communications networklogarithmic time segmented parallel prefix operations2D-gridIn this paper, we first describe a model for mapping the backpropagation artificial neural net learning algorithm onto a ...
A backpropagation algorithm, or backward propagation of errors, is analgorithmthat's used to help trainneural networkmodels. The algorithm adjusts the network's weights to minimize any gaps -- referred to as errors -- between predicted outputs and the actual target output. Weights are adjustable...
Paper Efficient Deep Learning with Decorrelated Backpropagation The backpropagation algorithm remains the dominant and most successful method for training deep neural networks (DNNs). At the same time, training DNNs at scale comes at a significant computational cost and therefore a high carbon foot...
In addition, in the conventional BP algorithm, the learning rate is fixed and that it is uniform for all the weights in a layer. In this paper, we propose an efficient acceleration technique, the backpropagation with adaptive learning rate and momentum term, which is based on the convent...
Error backpropagation training algorithm (BP) which is an iterative gradient descent algorithm is a simple way to train multilayer feedforward neural networks. Despite the popularity and effectiveness of this algorithm, its convergence is extremely slow. The main objective of this paper is to ...
The Csiszar family of error measures indicated in this paper offers an alternative set of error functions defined over a training set which can be adopted towards gradient-descent learnings in neural networks using the backpropagation algorithm in lieu of the conventional SE and/or RE error ...
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 biggest problem restricting the development of SNN is the training algorithm. Backpropagation (BP)-based training has extended SNNs to more complex network structures and datasets. However, the traditional design of BP ignores the dynamic characteristics of SNNs and is not biologically plausible. ...
With the proposed practical time-coding scheme, average delay response model, temporal error backpropagation algorithm, and heuristic loss function, "MT-Spike" achieves more efficient neural processing through flexible neural model size reduction while offering very competitive classification accuracy for ...
Elliot Humphrey January 10, 2024 10 min read Find Inspiration for Your Next Data Science Side Project Data Science Our weekly selection of must-read Editors’ Picks and original features TDS Editors March 9, 2023 3 min read How does a decision tree know the next best question to ask from ...