Активациона функција (Serbian Wikipedia)
Analysis of information sources in references of the Wikipedia article "Активациона функција" in Serbian language version.
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acm.org
dl.acm.org
- Nair, Vinod; Hinton, Geoffrey E. (2010), „Rectified Linear Units Improve Restricted Boltzmann Machines”, 27th International Conference on International Conference on Machine Learning, ICML'10, USA: Omnipress, стр. 807—814, ISBN 9781605589077
arxiv.org
- Abbott, L. F.; Sussillo, David (19. 12. 2014). „Random Walk Initialization for Training Very Deep Feedforward Networks” (на језику: енглески).
- Carlile, Brad; Delamarter, Guy; Kinney, Paul; Marti, Akiko; Whitney, Brian (9. 11. 2017). „Improving Deep Learning by Inverse Square Root Linear Units (ISRLUs)”. arXiv:1710.09967
[cs.LG]. - Eidnes, Lars; Nøkland, Arild (2018). „Shifting Mean Activation Towards Zero with Bipolar Activation Functions”. International Conference on Learning Representations (ICLR) Workshop. arXiv:1709.04054 .
- He, Kaiming; Zhang, Xiangyu; Ren, Shaoqing; Sun, Jian (6. 2. 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. 5. 2015). „Empirical Evaluation of Rectified Activations in Convolutional Network”. arXiv:1505.00853 [cs.LG].
- Clevert, Djork-Arné; Unterthiner, Thomas; Hochreiter, Sepp (23. 11. 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. 6. 2017). „Self-Normalizing Neural Networks”. Advances in Neural Information Processing Systems. 30 (2017). Bibcode:2017arXiv170602515K. arXiv:1706.02515 .
- Jin, Xiaojie; Xu, Chunyan; Feng, Jiashi; Wei, Yunchao; Xiong, Junjun; Yan, Shuicheng (22. 12. 2015). „Deep Learning with S-shaped Rectified Linear Activation Units”. arXiv:1512.07030 [cs.CV].
- Agostinelli, Forest; Hoffman, Matthew; Sadowski, Peter; Baldi, Pierre (21. 12. 2014). „Learning Activation Functions to Improve Deep Neural Networks”. arXiv:1412.6830 [cs.NE].
- Hendrycks, Dan; Gimpel, Kevin (2016). „Gaussian Error Linear Units (GELUs)”. arXiv:1606.08415 [cs.LG].
- Elfwing, Stefan; Uchibe, Eiji; Doya, Kenji (2017). „Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning”. arXiv:1702.03118 [cs.LG].
- Ramachandran, Prajit; Zoph, Barret; Le, Quoc V (2017). „Searching for Activation Functions”. arXiv:1710.05941 [cs.NE].
- Godfrey, Luke B.; Gashler, Michael S. (3. 2. 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. Bibcode:2016arXiv160201321G. arXiv:1602.01321 .
- Klimek, Matthew D.; Perelstein, Maxim (26. 10. 2018). „Neural Network-Based Approach to Phase Space Integration”. arXiv:1810.11509 [hep-ph].
- Gashler, Michael S.; Ashmore, Stephen C. (9. 5. 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 (2013). „Maxout Networks”. JMLR Workshop and Conference Proceedings. 28 (3): 1319—1327. Bibcode:2013arXiv1302.4389G. arXiv:1302.4389 .
books.google.com
- Mathematics of Control, Signals, and Systems: MCSS. (на језику: енглески). Springer International. 2006.
- Snyman, Jan (2005). Practical Mathematical Optimization: An Introduction to Basic Optimization Theory and Classical and New Gradient-Based Algorithms (на језику: енглески). Springer Science & Business Media. ISBN 9780387243481.
doi.org
- Wu, Huaiqin (9. 9. 2009). „Global stability analysis of a general class of discontinuous neural networks with linear growth activation functions”. Information Sciences. 179 (19): 3432—3441. ISSN 0020-0255. doi:10.1016/j.ins.2009.06.006.
harvard.edu
adsabs.harvard.edu
- Klambauer, Günter; Unterthiner, Thomas; Mayr, Andreas; Hochreiter, Sepp (8. 6. 2017). „Self-Normalizing Neural Networks”. Advances in Neural Information Processing Systems. 30 (2017). Bibcode:2017arXiv170602515K. arXiv:1706.02515 .
- Godfrey, Luke B.; Gashler, Michael S. (3. 2. 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. Bibcode:2016arXiv160201321G. arXiv:1602.01321 .
- Goodfellow, Ian J.; Warde-Farley, David; Mirza, Mehdi; Courville, Aaron; Bengio, Yoshua (2013). „Maxout Networks”. JMLR Workshop and Conference Proceedings. 28 (3): 1319—1327. Bibcode:2013arXiv1302.4389G. arXiv:1302.4389 .
ieee.org
ieeexplore.ieee.org
- Wuraola, Adedamola; Patel, Nitish (2018), „SQNL:A New Computationally Efficient Activation Function”, 2018 International Joint Conference on Neural Networks (IJCNN), Rio Rio de Janeiro, Brazil: IEEE, стр. 1—7
jmlr.org
- Glorot, Xavier; Bengio, Yoshua (2010), „Understanding the difficulty of training deep feedforward neural networks” (PDF), International Conference on Artificial Intelligence and Statistics (AISTATS'10), Society for Artificial Intelligence and Statistics, Архивирано из оригинала (PDF) 01. 04. 2017. г.
mathworks.com
- „elliotsig, Elliot symmetric sigmoid transfer function”, command introduced in Matlab R2012b, Matlab Documentation, MathWorks.
mlr.press
proceedings.mlr.press
- Glorot, Xavier; Bordes, Antoine; Bengio, Yoshua (2011). „Deep sparse rectifier neural networks” (PDF). International Conference on Artificial Intelligence and Statistics.
psu.edu
citeseerx.ist.psu.edu
- Elliot, David L. (1993), „A better activation function for artificial neural networks”, ISR Technical Report TR 93-8, University of Maryland, College Park, MD 20742., CiteSeerX 10.1.1.46.7204
sciencedirect.com
- Wu, Huaiqin (9. 9. 2009). „Global stability analysis of a general class of discontinuous neural networks with linear growth activation functions”. Information Sciences. 179 (19): 3432—3441. ISSN 0020-0255. doi:10.1016/j.ins.2009.06.006.
semanticscholar.org
pdfs.semanticscholar.org
- Maas, Andrew L.; Hannun, Awni Y.; Ng, Andrew Y. (јун 2013). „Rectifier nonlinearities improve neural network acoustic models” (PDF). Proc. ICML. 30 (1). Архивирано из оригинала (PDF) 03. 01. 2017. г. Приступљено 2. 1. 2017.
umontreal.ca
iro.umontreal.ca
- Bergstra, James; Desjardins, Guillaume; Lamblin, Pascal; Bengio, Yoshua (2009). „Quadratic polynomials learn better image features". Technical Report 1337”. Département d’Informatique et de Recherche Opérationnelle, Université de Montréal. Архивирано из оригинала 25. 9. 2018. г.
unicam.it
didattica.cs.unicam.it
- Hinkelmann, Knut. „Neural Networks,” (PDF). University of Applied Science Northwestern Switzerland. стр. 7. Архивирано из оригинала (PDF) 06. 10. 2018. г. Невалидан унос
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web.archive.org
- Hinkelmann, Knut. „Neural Networks,” (PDF). University of Applied Science Northwestern Switzerland. стр. 7. Архивирано из оригинала (PDF) 06. 10. 2018. г. Невалидан унос
|dead-url=dead(помоћ) - Bergstra, James; Desjardins, Guillaume; Lamblin, Pascal; Bengio, Yoshua (2009). „Quadratic polynomials learn better image features". Technical Report 1337”. Département d’Informatique et de Recherche Opérationnelle, Université de Montréal. Архивирано из оригинала 25. 9. 2018. г.
- Glorot, Xavier; Bengio, Yoshua (2010), „Understanding the difficulty of training deep feedforward neural networks” (PDF), International Conference on Artificial Intelligence and Statistics (AISTATS'10), Society for Artificial Intelligence and Statistics, Архивирано из оригинала (PDF) 01. 04. 2017. г.
- Maas, Andrew L.; Hannun, Awni Y.; Ng, Andrew Y. (јун 2013). „Rectifier nonlinearities improve neural network acoustic models” (PDF). Proc. ICML. 30 (1). Архивирано из оригинала (PDF) 03. 01. 2017. г. Приступљено 2. 1. 2017.
wikipedia.org
en.wikipedia.org
- Elliot, David L. (1993), „A better activation function for artificial neural networks”, ISR Technical Report TR 93-8, University of Maryland, College Park, MD 20742., CiteSeerX 10.1.1.46.7204
worldcat.org
- Wu, Huaiqin (9. 9. 2009). „Global stability analysis of a general class of discontinuous neural networks with linear growth activation functions”. Information Sciences. 179 (19): 3432—3441. ISSN 0020-0255. doi:10.1016/j.ins.2009.06.006.