1 |
Mnih, V., et al. (2013). Playing Atari with Deep Reinforcement Learning, NIPS Deep Learning Workshop 2013.
|
2 |
Szegedy, Christian, et al. (2015). Going deeper with convolutions, IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
|
3 |
Yang, J., Coughlin, J. F. (2014). In-vehicle technology for self-driving cars: Advantages and challenges for aging drivers, International Journal of Automotive Technology, vol.15, no.2, pp333-340.
DOI
|
4 |
Ziyu Wang, Tom Schaul, Matteo Hessel, Hado van Hasselt, Marc Lanctot, Nando de Freitas. (2016). Dueling Network Architectures for Deep Reinforcement Learning, Proceedings of the 33rd International 35 Conference on International Conference on Machine Learning (PMLR), Vol.48, pp1995-2003.
|
5 |
Simonyan, Karen, and Andrew Zisserman. (2015). Very deep convolutional networks for large-scale image recognition, arXivpreprint arXiv:1409.1556v6.
|
6 |
Lillicrap, Timothy P., et al. (2019). Continuous control with deep reinforcement learning, arXiv preprint arXiv:1509.02971v6.
|
7 |
Krizhevsky, A., et all. (2012). Imagenet Classification with Deep Convolutional Neural Networks, NIPS 2012.
|
8 |
Kim, J. Y., and Lee, S. J., et al. (2017). A Study on the National Policy Agenda based on Science & Tehnology.ICT for leading the 4th Industrial Revolution, Ministry of Science, ICT and Future Planning
|
9 |
Commercializations Promotion Agency for R&D Outcomes. (2019). Artificial intelligence (big data) market and technology trends, S&T Market Report, Vol. 71
|
10 |
Keskar, N. S., et all. (2017). on Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima, ICLR 2017.
|