Page 318 - Artificial Intelligence in the Age of Neural Networks and Brain Computing

References 311

[32] B. Zoph, Q.V. Le, Neural Architecture Search with Reinforcement Learning, 2016.
ArXiv 1611.01578.
[33] F. Gomez, R. Miikkulainen, Solving non-Markovian control tasks with neuroevolution,
in: Proceedings of the 16th International Joint Conference on Artificial Intelligence,
1999, pp. 1356e1361. San Francisco.
[34] J. Snoek, O. Rippel, K. Swersky, R. Kiros, N. Satish, N. Sundaram, M.M.A. Patwary,
M. Prabhat, R.P. Adams, Scalable Bayesian optimization using deep neural networks, in:
Proceedings of the International Conference on Machine Learning, 2015, pp. 2171e2180.
[35] A. Graves, N. Jaitly, Towards end-to-end speech recognition with recurrent neural net-
works, in: Proceedings of the 31st International Conference on Machine Learning,
2014, pp. 1764e1772.
[36] D. Bahdanau, K. Cho, Y. Bengio, Neural machine translation by jointly learning to align
and translate, in: Proceedings of International Conference on Learning Representations,
2015.
[37] J. Bayer, D. Wierstra, J. Togelius, J. Schmidhuber, Evolving memory cell structures for
sequence learning, in: Proceedings of International Conference on Artificial Neural
Networks, 2009, pp. 755e764.
[38] K. Cho, B. van Merrienboer, C. Gehre, F. Bougares, H. Schwenk, Y. Bengio, Learning
Phrase Representations Using RNN Encoder-Decoder for Statistical Machine Transla-
tion, 2014. ArXiv 1406.1078.
[39] R. Jozefowicz, W. Zaremba, I. Sutskever, An empirical exploration of recurrent network
architectures, in: Proceedings of the 32nd International Conference on Machine
Learning, 2015, pp. 2342e2350.
[40] G. Klaus, R. Srivastava, J. Koutnik, R. Steunebrink, J. Schmidhuber, Lstm: A Search
Space Odyssey, 2014. ArXiv 1503.04069.
[41] J. Chung, C. Gulcehre, K. Cho, Y. Bengio, Gated Feedback Recurrent Neural Networks,
2015. ArXiv 1502.02367.
[42] N. Kalchbrenner, I. Danihelka, A. Graves, Grid Long Short-Term Memory, 2015. ArXiv
1507.01526.
[43] J.G. Zilly, R.K. Srivastava, J. Koutnik, J. Schmidhuber, Recurrent Highway Networks,
2016. ArXiv 1607.03474.
[44] M.P. Marcus, M.A. Marcinkiewicz, B. Santorini, Building a large annotated corpus of
English: the Penn treebank, Computational Linguistics 19 (2) (1993).
[45] W. Zaremba, I. Sutskever, O. Vinyals, Recurrent Neural Network Regularization, 2014.
ArXiv 1409.2329.
[46] X. Chen, H. Fang, T.Y. Lin, R. Vedantam, S. Gupta, P. Dollar, C.L. Zitnick, Microsoft
Coco Captions: Data Collection and Evaluation Server, 2015. ArXiv 1504.00325.
[47] A. Karpathy, L. Fei-Fei, Deep visual-semantic alignments for generating image descrip-
tions, in: Proceedings of Computer Vision and Pattern Recognition Conference, 2015,
pp. 3128e3137.
[48] R. Vedantam, S. Bengio, K. Murphy, D. Parikh, G. Chechik, Context-Aware Captions
from Context-Agnostic Supervision, 2017. ArXiv 1701.02870.
[49] O. Vinyals, A. Toshev, S. Bengio, D. Erhan, Show and tell: a neural image caption
generator, in: Proceedings of Computer Vision and Pattern Recognition Conference,
2015, pp. 3156e3164.
[50] K. Xu, J. Ba, R. Kiros, K. Cho, A.C. Courville, R. Salkhutdinov, R.S. Zemel, Y. Bengio,
Show, attend and tell: neural image caption generation with visual attention, in:
Proceedings of the International Conference on Machine Learning, 2015, pp. 77e81.