參考文獻 |
A. L. Samuel, "Some Studies in Machine Learning Using the Game of Checkers," IBM Journal of Research and Develpoment, 3(3), pp.211-229, 1959.
[2] Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell, "Caffe: Convolutional Architecture for Fast Feature Embedding," arXiv preprint arXiv:1408.5093, 2014.
[3] T. Chen, M. Li, Y. Li, M. Lin, N, Wang, T. Xiao, B. Wu, C. Zhang, and Z. Zhang, "MXNet: A flexible and efficient machine learning library for heterogeneous distributed systems," arXiv preprint arXiv:1512.01274, 2015.
[4] M. Abadi et al., "TensorFlow: Large-scale machine learning on heterogeneous systems," 2015, software available from tensorflow.org.[Online]. Available: http://tensorflow.org/.
[5] R. Al-Rfou, G. Alain, A. Almahairi et al., "Theano: A Python framework for fast computation of mathematical expressions," arXiv preprint arXiv:1605.02688, 2016.
[6] R. Collobert, K. Kavukcuoglu, and C. Farabet, "Torch7: A matlab-like environment for machine learning," in Biglearn, NIPS Workshop, 2011.
[7] S. Dörner, et al., "Deep Learning Based Communication Over the Air," IEEE Journal of Selected Topics in Signal Processing, 2017
[8] T. J. O′shea and J. Hoydis, "An Introduction to Deep Learning for the Physical Layer," IEEE Transactions on Cognitive Communications and Networking, 2017.
[9] T. J. O′Shea, T. Erpek, and T.C. Clancy, "Deep Learning-Based MIMO Communications," arXiv preprint arXiv:1707.07980, 2017.
[10] Z. Xu, Y. Wang, J. Tang, J. Wang, and M. C. Gursoy, "A deep reinforcement learning based framework for power-efficient resource allocation in cloud RANs," in Proc. IEEE Int. Conf. Communications (ICC), May 2017, pp. 1-6.
[11] J. Kim, J. Park, J. Noh, S. Cho, "Completely Distributed Power Allocation using Deep Neural Network for Device to Device communication Underlaying LTE," arXiv preprint arXiv:1802.02736v2, 2018.
[12] Y. F. Chen, and J. W. Chen, "A Fast Subcarrier, Bit, and Power Allocation Algorithm for Multiuser OFDM-Based Systems," IEEE Transactions on Vehicular Technology, Vol. 57, NO.2, March, 2018.
[13] Nair, V. and G.E. Hinton, Rectified linear units improve restricted boltzmann machines, in Proceedings of the 27th International Conference on International Conference on Machine Learning. 2010, Omnipress: Haifa, Israel. p. 807-814.
[14] N. Srivastava, et al., "Dropout: A Simple Way to Prevent Neural Networks from Overfitting," Journal of Machine Learning Research,15(1), 1929-1958, 2014.
[15] J. M Torrance, and L. Hanzo, "Optimisation of switching levels for adaptive modulation in slow Rayleigh fading," Electronics Lett, vol. 32, no. 13, p.p. 1167-1168, 1996. |