激活函数 (Chinese Wikipedia)
Analysis of information sources in references of the Wikipedia article "激活函数" in Chinese language version.
refsWebsite
Global rank
Chinese rank
1st place
1st place
18th place
57th place
6,223rd place
9,933rd place
arxiv.org
- Carlile, Brad; Delamarter, Guy; Kinney, Paul; Marti, Akiko; Whitney, Brian. Improving Deep Learning by Inverse Square Root Linear Units (ISRLUs). 2017-11-09. arXiv:1710.09967
[cs.LG]. - He, Kaiming; Zhang, Xiangyu; Ren, Shaoqing; Sun, Jian. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. 2015-02-06. arXiv:1502.01852 [cs.CV].
- Xu, Bing; Wang, Naiyan; Chen, Tianqi; Li, Mu. Empirical Evaluation of Rectified Activations in Convolutional Network. 2015-05-04. arXiv:1505.00853 [cs.LG].
- Clevert, Djork-Arné; Unterthiner, Thomas; Hochreiter, Sepp. Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs). 2015-11-23. arXiv:1511.07289 [cs.LG].
- Klambauer, Günter; Unterthiner, Thomas; Mayr, Andreas; Hochreiter, Sepp. Self-Normalizing Neural Networks. 2017-06-08. arXiv:1706.02515 [cs.LG].
- Jin, Xiaojie; Xu, Chunyan; Feng, Jiashi; Wei, Yunchao; Xiong, Junjun; Yan, Shuicheng. Deep Learning with S-shaped Rectified Linear Activation Units. 2015-12-22. arXiv:1512.07030 [cs.CV].
- Forest Agostinelli; Matthew Hoffman; Peter Sadowski; Pierre Baldi. Learning Activation Functions to Improve Deep Neural Networks. 21 Dec 2014. arXiv:1412.6830 [cs.NE].
- Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning. [2018-06-13]. （原始内容存档于2018-06-13）.
- Searching for Activation Functions. [2018-06-13]. （原始内容存档于2018-06-13）.
- Godfrey, Luke B.; Gashler, Michael S. 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. 2016-02-03, 1602: 481–486. Bibcode:2016arXiv160201321G. arXiv:1602.01321 .
- Goodfellow, Ian J.; Warde-Farley, David; Mirza, Mehdi; Courville, Aaron; Bengio, Yoshua. Maxout Networks. JMLR WCP. 2013-02-18, 28 (3): 1319–1327. Bibcode:2013arXiv1302.4389G. arXiv:1302.4389 .
harvard.edu
ui.adsabs.harvard.edu
- Godfrey, Luke B.; Gashler, Michael S. 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. 2016-02-03, 1602: 481–486. Bibcode:2016arXiv160201321G. arXiv:1602.01321 .
- Goodfellow, Ian J.; Warde-Farley, David; Mirza, Mehdi; Courville, Aaron; Bengio, Yoshua. Maxout Networks. JMLR WCP. 2013-02-18, 28 (3): 1319–1327. Bibcode:2013arXiv1302.4389G. arXiv:1302.4389 .
jmlr.org
- Glorot, Xavier; Bengio, Yoshua, 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, 2010, （原始内容存档 (PDF)于2017-04-01）
mlr.press
proceedings.mlr.press
- Glorot, Xavier; Bordes, Antoine; Bengio, Yoshua. Deep sparse rectifier neural networks (PDF). International Conference on Artificial Intelligence and Statistics. 2011. （原始内容存档 (PDF)于2018-06-19）.
umontreal.ca
iro.umontreal.ca
- Bergstra, James; Desjardins, Guillaume; Lamblin, Pascal; Bengio, Yoshua. Quadratic polynomials learn better image features". Technical Report 1337. Département d’Informatique et de Recherche Opérationnelle, Université de Montréal. 2009. （原始内容存档于2018-09-25）.
web.archive.org
- Bergstra, James; Desjardins, Guillaume; Lamblin, Pascal; Bengio, Yoshua. Quadratic polynomials learn better image features". Technical Report 1337. Département d’Informatique et de Recherche Opérationnelle, Université de Montréal. 2009. （原始内容存档于2018-09-25）.
- Glorot, Xavier; Bengio, Yoshua, 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, 2010, （原始内容存档 (PDF)于2017-04-01）
- Glorot, Xavier; Bordes, Antoine; Bengio, Yoshua. Deep sparse rectifier neural networks (PDF). International Conference on Artificial Intelligence and Statistics. 2011. （原始内容存档 (PDF)于2018-06-19）.
- Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning. [2018-06-13]. （原始内容存档于2018-06-13）.
- Searching for Activation Functions. [2018-06-13]. （原始内容存档于2018-06-13）.