Formal Derivation of Mesh Neural Networks with Their Forward-Only Gradient Propagation

Author:

Galatolo Federico A.,Cimino Mario G. C. A.ORCID,Vaglini Gigliola

Funder

Regione Toscana

Università di Pisa

Ministero dell’Istruzione, dell’Università e della Ricerca

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Networks and Communications,General Neuroscience,Software

Reference22 articles.

1. Galatolo F, Cimino M, Vaglini G (2019) Using stigmergy as a computational memory in the design of recurrent neural networks. In: Proceedings of the 8th international conference on pattern recognition applications and methods

2. Wilamowski BM, Yu H (2010) Neural network learning without backpropagation. IEEE Trans Neural Netw 21:1793–1803

3. Guo W, Huang H, Huang T (2017) Complex-valued feedforward neural networks learning without backpropagation. In: Liu D, Xie S, Li Y, Zhao D, El-Alfy E-SM (eds) Neural information processing. Springer, Cham, pp 100–107

4. Ma KW-D, Lewis JP, Kleijn WB (2019) The hsic bottleneck: deep learning without back-propagation, ArXiv:1908.01580v3

5. Jaderberg M, Czarnecki WM, Osindero S, Vinyals O, Graves A, Silver D, Kavukcuoglu K (2017) Decoupled neural interfaces using synthetic gradients. In: Proceedings of the 34th international conference on machine learning, Volume 70, pp. 1627–1635, JMLR.org

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