Learning in the machine: to share or not to share?

DOI10.1016/j.neunet.2020.03.016zbMath1468.68186arXiv1909.11483OpenAlexW3013796640WikidataQ91664177 ScholiaQ91664177MaRDI QIDQ1980412

Pierre Baldi, Erik Linstead, Nicholas LaHaye, Jordan Ott

Publication date: 8 September 2021

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1909.11483

zbMATH Keywords

convolutional neural networksdeep learningweight-sharingbiologically plausible architectures

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07)

Uses Software

• MNIST
• TensorFlow
• Keras
• ImageNet
• GitHub
• AlexNet

Cites Work

• Unnamed Item
• The dropout learning algorithm
• Neocognition: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position
• Learning in the machine: random backpropagation and the deep learning channel
• Learning in the machine: the symmetries of the deep learning channel
• Deep Learning with Dynamic Spiking Neurons and Fixed Feedback Weights