David Zoltowski, Jonathan Pillow, and Scott Linderman. A general recurrent state space framework for modeling neural dynamics during decision-making. In International Conference on Machine Learning, pages 11680-11691. PMLR, 2020.
Tamara G Kolda and Brett W Bader. Tensor decompositions and applications. SIAM review, 51(3): 455-500, 2009.
James Douglas Hamilton. Time series analysis. Princeton University Press, 2020.
Igor Melnyk and Arindam Banerjee. Estimating structured vector autoregressive models. In Maria Florina Balcan and Kilian Q. Weinberger, editors, Proceedings of The 33rd International Conference on Machine Learning, volume 48 of Proceedings of Machine Learning Research, pages 830-839, New York, New York, USA, 20-22 Jun 2016. PMLR.
Shawn Ni and Dongchu Sun. Bayesian estimates for vector autoregressive models. Journal of Business & Economic Statistics, 23(1):105-117, 2005.
Kevin P Murphy. Machine learning: a probabilistic perspective. MIT press, 2012.
Uri Nahum Lerner. Hybrid Bayesian networks for reasoning about complex systems. PhD thesis, Stanford University, 2003.
Arnaud Doucet, Neil J Gordon, and Vikram Krishnamurthy. Particle filters for state estimation of jump Markov linear systems. IEEE Transactions on signal processing, 49(3):613-624, 2001.
Mark H A Davis and Richard B Vinter. Stochastic modelling and control. Chapman and Hall London; New York, 1985. ISBN 0412162008.
Di Wang, Yao Zheng, Heng Lian, and Guodong Li. High-dimensional vector autoregressive time series modeling via tensor decomposition. Journal of the American Statistical Association, pages 1-19, 2021.
Kameron Decker Harris, Aleksandr Aravkin, Rajesh Rao, and Bingni Wen Brunton. Time-varying autoregression with low-rank tensors. SIAM Journal on Applied Dynamical Systems, 20(4): 2335-2358, 2021.
Wei Zhang, Ivor Cribben, Sonia Petrone, and Michele Guindani. Bayesian time-varying tensor vector autoregressive models for dynamic effective connectivity. arXiv preprint arXiv:2106.14083, 2021.
Alexander Novikov, Dmitrii Podoprikhin, Anton Osokin, and Dmitry P Vetrov. Tensorizing neural networks. Advances in Neural Information Processing Systems, 28, 2015.
Ivan V Oseledets. Tensor-train decomposition. SIAM Journal on Scientific Computing, 33(5): 2295-2317, 2011.
Rose Yu, Stephan Zheng, Anima Anandkumar, and Yisong Yue. Long-term forecasting using higher order tensor RNNs. arXiv preprint arXiv:1711.00073, 2017.
Hejia Qiu, Chao Li, Ying Weng, Zhun Sun, Xingyu He, and Qibin Zhao. On the memory mechanism of tensor-power recurrent models. In International Conference on Artificial Intelligence and Statistics, pages 3682-3690. PMLR, 2021.
Adrian Valente, Srdjan Ostojic, and Jonathan W Pillow. Probing the relationship between latent linear dynamical systems and low-rank recurrent neural network models. *Neural Computation*, 34(9): 1871-1892, 2022.
Saul Kato, Harris S Kaplan, Tina Schrödel, Susanne Skora, Theodore H Lindsay, Eviatar Yemini, Shawn Lockery, and Manuel Zimmer. Global brain dynamics embed the motor command sequence of Caenorhabditis elegans. Cell, 163(3):656-669, 2015.
Jesse M Gray, Joseph J Hill, and Cornelia I Bargmann. A circuit for navigation in Caenorhabditis elegans. Proceedings of the National Academy of Sciences, 102(9):3184-3191, 2005.
Harris S Kaplan, Oriana Salazar Thula, Niklas Khoss, and Manuel Zimmer. Nested neuronal dynamics orchestrate a behavioral hierarchy across timescales. Neuron, 105(3):562-576, 2020.