Gradient target propagation

October 19, 2018 · Entered Twilight · 🏛 arXiv.org

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Repo contents: README, data, main.py, target.py, test.py, todo list

Authors Tiago de Souza Farias, Jonas Maziero arXiv ID 1810.09284 Category cs.LG: Machine Learning Citations 3 Venue arXiv.org Repository https://github.com/tiago939/target ⭐ 3 Last Checked 4 months ago
Abstract
We report a learning rule for neural networks that computes how much each neuron should contribute to minimize a giving cost function via the estimation of its target value. By theoretical analysis, we show that this learning rule contains backpropagation, Hebian learning, and additional terms. We also give a general technique for weights initialization. Our results are at least as good as those obtained with backpropagation. The neural networks are trained and tested in three problems: MNIST, MNIST-Fashion, and CIFAR-10 datasets. The associated code is available at https://github.com/tiago939/target.
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