Training multi-layered neural network neocognitron

By Kunihiko Fukushima. 2013

63 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Media Technology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Frank Emmert-Streib
2. Zhenyi Yang
3. Matthias Dehmer
4. Han Feng
5. Shailesh Tripathi
6. J. Urgen Schmidhuber
7. David R Ha
8. Alex Graves

In Media Technology

1. Frank Emmert-Streib
2. J. Urgen Schmidhuber
3. Zhenyi Yang
4. Matthias Dehmer
5. Han Feng
6. Shailesh Tripathi

Citations in the corpus

• Deep learning in neural networks: An overview (2014, methodology): “l and downsampling layers (e.g., Sec. 5.19–5.22). Fukushima, however, did not set the weights by supervised backpropagation (Sec. 5.5, 5.8), but by local, WTA-based unsupervised learning rules (e.g., Fukushima, 2013b), or by prewiring. In that sense he did not care for the DL problem (Sec. 5.9), alt”
• An Introductory Review of Deep Learning for Prediction Models With Big Data (2020, methodology): “In this paper, we provided an introductory review for deep learning models including Deep Feedforward Neural Networks, (D-FFNN), Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), Autoencoders (AE) and Long Short-Term Memory networks (LSTMs).”