Tensorial Recurrent Neural Networks for Longitudinal Data Analysis

J. L. Elman, “Finding structure in time,” Cognitive Science , vol. 14, no. 2, pp. 179–211, 1990

1990

Earlier work this paper cites.

M. I. Jordan, Serial Order: A Parallel Distributed Processing Approach , ser. Advances in Psychology: Neural-Network Models of Cognition. Elsevier, 1997, vol. 121, ch. 25, pp. 471–495

1997

Earlier work this paper cites.

S. Hochreiter and J. Schmidhuber, “Long short-term memory”. .” Neural Computation , vol. 9, no. 8, pp. 1735–1780, 1997

1997

Earlier work this paper cites.

F. A. Gers, J. Schmidhuber, and F. Cummins, “Learning to forget: Continual prediction with LSTM,” Neural Computation , vol. 12, no. 10, pp. 2451–2471, 2000

2000

Earlier work this paper cites.

T. G. Kolda, “Multilinear Operators for Higher-Order Decompositions,” Sandia National Laboratories, Technical report, 2006

2006

Earlier work this paper cites.

T. G. Kolda and B. W. Bader, “Tensor decompositions and applications,” SIAM Review , vol. 51, no. 3, pp. 455–500, 2009

2009

Earlier work this paper cites.

A. Graves, A. rahman Mohamed, and G. Hinton, “Speech recognition with deep recurrent neural networks,” in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2013

2013

Earlier work this paper cites.

K. Cho, B. van Merriënboer, Ç. Gülçehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, “Learning phrase representations using rnn encoder–decoder for statistical machine translation,” in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) . Doha, Qatar: Association for Computational Linguistics, Oct. 2014, pp. 1724–1734. [Online]. Available: http://www.aclweb.org/anthology/D14-1179

2014

Cited alongside, same era.

C. Ionescu, O. Vantzos, and C. Sminchisescu, “Matrix backpropagation for deep networks with structured layers,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV) , 2015, pp. 2965–2973

2015

Cited alongside, same era.

W. D. Mulder, S. Bethard, and M.-F. Moens, “A survey on the application of recurrent neural networks to statistical language modeling,” Computer Speech & Language , vol. 30, no. 1, pp. 61–98, 2015

2015

Cited alongside, same era.

P. D. Hoff, “Multilinear Tensor Regression for Longitudinal Relational Data,” Ann. Appl. Stat. , vol. 9, no. 3, pp. 1169–1193, 2015

J. Gao, Y. Guo, and Z. Wang, “Matrix neural networks,” in Proceedings of the14th International Symposium on Neural Networks (ISNN) , vol. Accepted, Sapporo, Japan, 2017, pp. 1–10

2017

Closest in time.

M. Bai, B. Zhang, and J. Gao, “Tensorial neural networks and its application in longitudinal network data analysis,” in submitted to the 24th International Conference On Neural Information Processing (ICONIP) , 2017

2017

Closest in time.

J.-T. Chien and Y.-T. Bao, “Tensor-factorized neural networks,” IEEE Transactions on Neural Networks and Learning Systems , vol. PP, 2017. [Online]. Available: http://ieeexplore.ieee.org/document/7902201/

2017

Closest in time.

Y. Seo, M. Defferrard, P. Vandergheynst, and X. Bresson, “Structured sequence modeling with graph convolutional recurrent networks,” in Proceedings of International Conference on Learning Representation , 2017

2017

Closest in time.