Page 115 - IJB-5-1
P. 115
Sun J, et al.
Polymers, 9(9): 434. Networks for Large–scale Image Recognition. In Process
14. Sun J, Hong G S, Rahman M, et al., 2005, Improved International Conference on Learning Representations http://
performance evaluation of tool condition identification by arxiv.org/abs/1409.1556.
manufacturing loss consideration. Int J Prod Res, 43(6): 19. Raje P V, Murmu N C, 2014, A review on electrohydrodynamic–
1185–1204. inkjet printing technology. Int J Emerg Technol Adv Eng,
15. Cireşan, D, Meier, U, Schmidhuber, J, 2012, Multi-Column 4(5): 174–183.
Deep Neural Networks for Image Classification. 2012 IEEE 20. Linzhi J, Xiang W, Hang L, et al., 2018, Zein Increases the
Conf.Comput Vis Pattern Recognit, 2012: 3642-3649. cytoaffinity and biodegradability of scaffolds 3D–printed
16. LeCun Y, Bengio Y, Hinton G, 2015, Deep learning. Nature, with zein and poly (ε–caprolactone) composite ink. ACS Appl
521(7553): 436. Mater Interfaces, 10(22): 18551–18559. https: doi: 10.1021/
17. Fu J L, Zheng H L, Mei T, et al., 2017, Look closer to see acsami.8b04344.
better: Recurrent attention convolutional neural network for 21. TensorFlow. Available from: https://www.tensorflow.org.
fine–grained image recognition. 2017 IEEE Conf.Comput Vis [Last accessed on 2018 Jul 14].
Pattern Recognit, 2017: 4476–4484. https://doi.org/10.1109/ 22. Srivastava N, Hinton G, Krizhevsky A, et al., 2014, Dropout:
CVPR.2017.476. A simple way to prevent neural networks from overfitting.
18. Simonyan K, Zisserman A, 2014, Very Deep Convolutional J Mach Learn Res, 15(1): 1929–1958.
International Journal of Bioprinting (2019)–Volume 5, Issue 1 9

