Передавальна функція штучного нейрона (Ukrainian Wikipedia)

Analysis of information sources in references of the Wikipedia article "Передавальна функція штучного нейрона" in Ukrainian language version.

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acm.org

dl.acm.org

arxiv.org

  • Carlile, Brad; Delamarter, Guy; Kinney, Paul; Marti, Akiko; Whitney, Brian (9 листопада 2017). Improving Deep Learning by Inverse Square Root Linear Units (ISRLUs). arXiv:1710.09967 [cs.LG].
  • He, Kaiming; Zhang, Xiangyu; Ren, Shaoqing; Sun, Jian (6 лютого 2015). Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv:1502.01852 [cs.CV].
  • Xu, Bing; Wang, Naiyan; Chen, Tianqi; Li, Mu (4 травня 2015). Empirical Evaluation of Rectified Activations in Convolutional Network. arXiv:1505.00853 [cs.LG].
  • Clevert, Djork-Arné; Unterthiner, Thomas; Hochreiter, Sepp (23 листопада 2015). Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs). arXiv:1511.07289 [cs.LG].
  • Klambauer, Günter; Unterthiner, Thomas; Mayr, Andreas; Hochreiter, Sepp (8 червня 2017). Self-Normalizing Neural Networks. arXiv:1706.02515 [cs.LG].
  • Jin, Xiaojie; Xu, Chunyan; Feng, Jiashi; Wei, Yunchao; Xiong, Junjun; Yan, Shuicheng (22 грудня 2015). Deep Learning with S-shaped Rectified Linear Activation Units. arXiv:1512.07030 [cs.CV].
  • Forest Agostinelli; Matthew Hoffman; Peter Sadowski; Pierre Baldi (21 грудня 2014). Learning Activation Functions to Improve Deep Neural Networks. arXiv:1412.6830 [cs.NE].
  • Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning
  • Searching for Activation Functions
  • Godfrey, Luke B.; Gashler, Michael S. (3 лютого 2016). A continuum among logarithmic, linear, and exponential functions, and its potential to improve generalization in neural networks. 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management: KDIR. 1602: 481—486. arXiv:1602.01321. Bibcode:2016arXiv160201321G.
  • Gashler, Michael S.; Ashmore, Stephen C. (9 травня 2014). Training Deep Fourier Neural Networks To Fit Time-Series Data. arXiv:1405.2262 [cs.NE].
  • Goodfellow, Ian J.; Warde-Farley, David; Mirza, Mehdi; Courville, Aaron; Bengio, Yoshua (18 лютого 2013). Maxout Networks. JMLR WCP. 28 (3): 1319—1327. arXiv:1302.4389. Bibcode:2013arXiv1302.4389G.

books.google.com

doi.org

  • Ke-Lin Du, Swamy M. N. S., Neural Networks and Statistical Learning, Springer-Verlag London, 2014 doi:10.1007/978-1-4471-5571-3
  • Lionel Tarassenko, 2 - Mathematical background for neural computing, In Guide to Neural Computing Applications, Butterworth-Heinemann, New York, 1998, Pages 5-35, ISBN 9780340705896, http://doi.org/10.1016/B978-034070589-6/50002-6.
  • Anthony, Martin (2001). 1. Artificial Neural Networks: 1—8. doi:10.1137/1.9780898718539.
  • Stegemann, J. A.; N. R. Buenfeld (2014). A Glossary of Basic Neural Network Terminology for Regression Problems. Neural Computing & Applications. 8 (4): 290—296. doi:10.1007/s005210050034. ISSN 0941-0643.

harvard.edu

ui.adsabs.harvard.edu

  • Godfrey, Luke B.; Gashler, Michael S. (3 лютого 2016). A continuum among logarithmic, linear, and exponential functions, and its potential to improve generalization in neural networks. 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management: KDIR. 1602: 481—486. arXiv:1602.01321. Bibcode:2016arXiv160201321G.
  • Goodfellow, Ian J.; Warde-Farley, David; Mirza, Mehdi; Courville, Aaron; Bengio, Yoshua (18 лютого 2013). Maxout Networks. JMLR WCP. 28 (3): 1319—1327. arXiv:1302.4389. Bibcode:2013arXiv1302.4389G.

jmlr.org

mlr.press

proceedings.mlr.press

neuralnetworksanddeeplearning.com

semanticscholar.org

pdfs.semanticscholar.org

umontreal.ca

iro.umontreal.ca

web.archive.org

worldcat.org

search.worldcat.org

  • Stegemann, J. A.; N. R. Buenfeld (2014). A Glossary of Basic Neural Network Terminology for Regression Problems. Neural Computing & Applications. 8 (4): 290—296. doi:10.1007/s005210050034. ISSN 0941-0643.