Читать книгу Artificial Intelligence and Quantum Computing for Advanced Wireless Networks - Savo G. Glisic - Страница 93

References

Оглавление

1 1 Scarselli, F., Gori, M., Tsoi, A.C. et al. (2009). The graph neural network model. IEEE Trans. Neural Netw. 20 (1): 61–80.

2 2 Khamsi, M.A. and Kirk, W.A. (2011). An Introduction to Metric Spaces and Fixed Point Theory, vol. 53. Wiley.

3 3 M. Kampffmeyer, Y. Chen, X. Liang, H. Wang, Y. Zhang, and E. P. Xing, “Rethinking knowledge graph propagation for zero‐shot learning,” arXiv preprint arXiv:1805.11724, 2018.

4 4 Y. Zhang, Y. Xiong, X. Kong, S. Li, J. Mi, and Y. Zhu, “Deep collective classification in heterogeneous information networks,” in WWW 2018, 2018, pp. 399–408.

5 5 X. Wang, H. Ji, C. Shi, B. Wang, Y. Ye, P. Cui, and P. S. Yu, “Heterogeneous graph attention network,” WWW 2019, 2019.

6 6 D. Beck, G. Haffari, and T. Cohn, “Graph‐to‐sequence learning using gated graph neural networks,” in ACL 2018, 2018, pp. 273–283.

7 7 M. Schlichtkrull, T. N. Kipf, P. Bloem, R. van den Berg, I. Titov, and M. Welling, “Modeling relational data with graph convolutional networks,” in ESWC 2018. Springer, 2018, pp. 593–607

8 8 Y. Li, R. Yu, C. Shahabi, and Y. Liu, “Diffusion convolutional recurrent neural network: Data‐driven traffic forecasting,” arXiv preprint arXiv:1707.01926, 2017.

9 9 B. Yu, H. Yin, and Z. Zhu, “Spatio‐temporal graph convolutional networks: A deep learning framework for traffic forecasting,” arXiv preprint arXiv:1709.04875, 2017. 20

10 10 A. Jain, A. R. Zamir, S. Savarese, and A. Saxena, “Structural‐rnn: Deep learning on spatio‐temporal graphs,” in CVPR 2016, 2016, pp. 5308–5317.

11 11 S. Yan, Y. Xiong, and D. Lin, “Spatial temporal graph convolutional networks for skeleton‐based action recognition,” in Thirty Second AAAI Conference on Artificial Intelligence, 2018.

12 12 J. Bruna, W. Zaremba, A. Szlam, and Y. Lecun, “Spectral networks and locally connected networks on graphs,” ICLR 2014, 2014.

13 13 Hammond, D.K., Vandergheynst, P., and Gribonval, R. (2011). Wavelets on graphs via spectral graph theory. Appl. Comput. Harmonic Anal. 30 (2): 129–150.

14 14 T. N. Kipf and M. Welling, “Semi‐supervised classification with graph convolutional networks,” in Proc. of ICLR 2017, 2017.

15 15 D. K. Duvenaud, D. Maclaurin, J. Aguileraiparraguirre, R. Gomezbombarelli, T. D. Hirzel, A. Aspuruguzik, and R. P. Adams, “Convolutional networks on graphs for learning molecular fingerprints,” NIPS 2015, pp. 2224–2232, 2015.

16 16 J. Atwood and D. Towsley, “Diffusion‐convolutional neural networks,” in Proc. of NIPS 2016, 2016, pp. 1993–2001.

17 17 C. Zhuang and Q. Ma, “Dual graph convolutional networks for graph‐based semi‐supervised classification,” in WWW 2018, 2018.

18 18 S. Cao, W. Lu, and Q. Xu. Grarep: Learning graph representations with global structural information. In KDD, 2015.

19 19 A. Grover and J. Leskovec. node2vec: Scalable feature learning for networks. In KDD, 2016.

20 20 B. Perozzi, R. Al‐Rfou, and S. Skiena. Deepwalk: Online learning of social representations. In KDD, 2014

21 21 J. Tang, M. Qu, M. Wang, M. Zhang, J. Yan, and Q. Mei. Line: Large‐scale information network embedding. In WWW, 2015.

22 22 D. Wang, Daixin Wang, Peng Cui, Wenwu Zhu Structural deep network embedding. In KDD, 2016.

23 23 W. L. Hamilton, Z. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” NIPS 2017, pp. 1024–1034, 2017. https://arxiv.org/pdf/1706.02216.pdf

24 24 K. He, X. Zhang, S. Ren, and J. Sun, “Identity mappings in deep residual networks,” in ECCV 2016. Springer, 2016, pp. 630–645.

25 25 K. Cho, B. Van Merrienboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, “Learning phrase representations using rnn encoder–decoder for statistical machine translation,” EMNLP 2014, pp. 1724–1734, 2014.

26 26 Hochreiter, S. and Schmidhuber, J. (1997). Long short‐term memory. Neural Comput. 9 (8): 1735–1780.

27 27 Y. Li, D. Tarlow, M. Brockschmidt, and R. S. Zemel, “Gated graph sequence neural networks,” arXiv: Learning, 2016.

28 28 K. S. Tai, R. Socher, and C. D. Manning, “Improved semantic representations from tree‐structured long short‐term memory networks,” IJCNLP 2015, pp. 1556–1566, 2015.

29 29 V. Zayats and M. Ostendorf, “Conversation modeling on reddit using a graph‐structured lstm,” TACL 2018, vol. 6, pp. 121–132, 2018.

30 30 N. Peng, H. Poon, C. Quirk, K. Toutanova, and W.‐t. Yih,“Cross‐sentence n‐ary relation extraction with graph lstms,” arXiv preprint arXiv:1708.03743, 2017.

31 31 D. Bahdanau, K. Cho, and Y. Bengio, “Neural machine translation by jointly learning to align and translate,” ICLR 2015, 2015.

32 32 J. Gehring, M. Auli, D. Grangier, and Y. N. Dauphin, “A convolutional encoder model for neural machine translation,” ACL 2017,vol. 1, pp. 123–135,

33 33 A. Vaswani, N. Shazeer, N. Parmar, L. Jones, J. Uszkoreit, A. N.Gomez, and L. Kaiser, “Attention is all you need,” NIPS 2017, pp.5998–6008, 2017.

34 34 J. Cheng, L. Dong, and M. Lapata, “Long short‐term memory‐networks for machine reading,” EMNLP 2016, pp. 551–561, 2016.

35 35 P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio, “Graph attention networks,” ICLR 2018, 2018.

36 36 J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, and G. E. Dahl, “Neural message passing for quantum chemistry,” arXiv preprint arXiv:1704.01212, 2017.

37 37 P. W. Battaglia, J. B. Hamrick, V. Bapst, A. Sanchez‐Gonzalez, V. Zambaldi, M. Malinowski, A. Tacchetti, D. Raposo, A. Santoro, R. Faulkner et al., “Relational inductive biases, deep learning, and graph networks,” arXiv preprint arXiv:1806.01261, 2018.

38 38 Z. Wu, Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, Philip S. Yu, A Comprehensive Survey on Graph Neural Networks, arXiv:1901.00596v4 [cs.LG] 4 Dec 2019, also in in IEEE Transactions on Neural Networks and Learning Systems, doi: 10.1109/TNNLS.2020.2978386

39 39 D. V. Tran, A. Sperduti Dinh V. Tran, Nicol'o Navarin, Alessandro Sperduti, “On filter size in graph convolutional networks,” in SSCI. IEEE, 2018, pp. 1534–1541.

40 40 C. Gallicchio and A. Micheli, “Graph echo state networks,” in IJCNN. IEEE, 2010, pp. 1–8

41 41 Y. Li, D. Tarlow, M. Brockschmidt, and R. Zemel, “Gated graph sequence neural networks,” in Proc. of ICLR, 2015

42 42 K. Cho, B. Van Merrienboer, C. Gulcehre, D. Bahdanau, F. Bougares, ¨ H. Schwenk, and Y. Bengio,“Learning phrase representations using rnn encoder‐decoder for statistical machine translation,” in Proc. of EMNLP, 2014, pp. 1724–1734.

43 43 H. Dai, Z. Kozareva, B. Dai, A. Smola, and L. Song, “Learning steadystates of iterative algorithms over graphs,” in Proc. of ICML, 2018, pp. 1114–1122.

44 44 Shuman, D.I., Narang, S.K., Frossard, P. et al. (2013). The emerging field of signal processing on graphs: extending high‐dimensional data analysis to networks and other irregular domains. IEEE Signal Process. Mag. 30 (3): 83–98.

45 45 M. Defferrard, X. Bresson, and P. Vandergheynst, “Convolutional neural networks on graphs with fast localized spectral filtering,” in Proc. of NIPS, 2016, pp. 3844–3852.

46 46 M. Henaff, J. Bruna, and Y. LeCun, “Deep convolutional networks on graph‐structured data,” arXiv preprint arXiv:1506.05163, 2015.

47 47 Levie, R., Monti, F., Bresson, X., and Bronstein, M.M. (2017). Cayleynets: graph convolutional neural networks with complex rational spectral filters. IEEE Trans. Signal Process. 67 (1): 97–109.

48 48 Micheli, A. (2009). Neural network for graphs: a contextual constructive approach. IEEE Trans. Neural Netw. 20 (3): 498–511.

49 49 Y. Li, R. Yu, C. Shahabi, and Y. Liu, “Diffusion convolutional recurrent neural network: Data‐driven traffic forecasting,” in Proc. of ICLR, 2018

50 50 S. Yan, Y. Xiong, and D. Lin, “Spatial temporal graph convolutional networks for skeleton‐based action recognition,” in Proc. of AAAI, 2018.

51 51 J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, and G. E. Dahl, “Neural message passing for quantum chemistry,” in Proc. of ICML, 2017, pp. 1263–1272.

52 52 K. Xu, W. Hu, J. Leskovec, and S. Jegelka, “How powerful are graph neural networks,” in Proc. of ICLR, 2019

53 53 P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio, “Graph attention networks,” in Proc. of ICLR, 2017

54 54 S. Cao, W. Lu, and Q. Xu, “Deep neural networks for learning graph representations,” in Proc. of AAAI, 2016, pp. 1145–1152

55 55 D. Wang, P. Cui, and W. Zhu, “Structural deep network embedding,” in Proc. of KDD. ACM, 2016, pp. 1225–1234.

56 56 T. N. Kipf and M. Welling, “Variational graph auto‐encoders,” NIPS Workshop on Bayesian Deep Learning, 2016.

57 57 K. Tu, P. Cui, X. Wang, P. S. Yu, and W. Zhu, “Deep recursive network embedding with regular equivalence,” in Proc. of KDD. ACM, 2018, pp. 2357–2366.

58 58 W. Yu, C. Zheng, W. Cheng, C. C. Aggarwal, D. Song, B. Zong, H. Chen, and W. Wang, “Learning deep network representations with adversarially regularized autoencoders,” in Proc. of AAAI. ACM, 2018, pp. 2663–2671.

59 59 Y. Li, O. Vinyals, C. Dyer, R. Pascanu, and P. Battaglia, “Learning deep generative models of graphs,” in Proc. of ICML, 2018.

60 60 M. Simonovsky and N. Komodakis, “Graphvae: Towards generation of small graphs using variational autoencoders,” in ICANN. Springer, 2018, pp. 412–422

61 61 Z. Wu, S. Pan, G. Long, J. Jiang, and C. Zhang, “Graph wavenet for deep spatial‐temporal graph modeling,” in Proc. of IJCAI, 2019

62 62 Khamsi, M.A. (2001). An Introduction to Metric Spaces and Fixed Point Theory. New York: Wiley.

63 63 Powell, M.J.D. (1964). An efficient method for finding the minimum of a function of several variables without calculating derivatives. Comput. J. 7: 155–162.

64 64 Frasconi, P., Gori, M., and Sperduti, A. (1998). A general framework for adaptive processing of data structures. IEEE Trans. Neural Netw. 9 (5): 768–786.

65 65 L. Almeida, “A learning rule for asynchronous perceptrons with feedback in a combinatorial environment,” in Proc. IEEE Int. Conf. Neural Netw., M. Caudill and C. Butler, Eds., San Diego, 1987, vol. 2, pp. 609–618.

66 66 Pineda, F. (1987). Generalization of back‐propagation to recurrent neural networks. Phys. Rev. Lett. 59: 2229–2232.

67 67 Graham, A. (1982). Kronecker Products and Matrix Calculus: With Applications. New York: Wiley.

68 68 R. Singh, A. Chakraborty and B. S. Manoj, Graph Fourier Transform based on Directed Laplacian, https://arxiv.org/pdf/1601.03204.pdf

Artificial Intelligence and Quantum Computing for Advanced Wireless Networks

Подняться наверх