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Learning accurate predictive models of real-world dynamic phenomena (e.g., climate, biological) remains a challenging task.
Stochastic processes and filtering theory
Jazwinski, A. H · 1970
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Fixed interval smoothing for nonlinear continuous time systems
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PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals
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Linear estimation
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Stochastic differential equations
Øksendal, B · 2003
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Jorgensen, J. B., Thomsen, P. G., Madsen, H., and Kristensen, M. R · 2007
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Long-term daily and monthly climate records from stations across the contiguous united states
Menne, M., Williams Jr, C., and Vose, R · 2010
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Matrix Riccati equations in control and systems theory
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Gaussian filtering and smoothing for continuous-discrete dynamic systems
Särkkä, S. and Sarmavuori, J · 2013
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Chung, J., Kastner, K., Dinh, L., Goel, K., Courville, A. C., and Bengio, Y · 2015
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Krishnan, R. G., Shalit, U., and Sontag, D · 2015
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Deep variational Bayes filters: Unsupervised learning of state space models from raw data
Karl, M., Soelch, M., Bayer, J., and Van der Smagt, P · 2016
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Patient subtyping via time-aware LSTM networks
Baytas, I. M., Xiao, C., Zhang, X., Wang, F., Jain, A. K., and Zhou, J · 2017
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A disentangled recognition and nonlinear dynamics model for unsupervised learning
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Structured inference networks for nonlinear state space models
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Recurrent neural networks for multivariate time series with missing values
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Doerr, A., Daniel, C., Schiegg, M., Duy, N.-T., Schaal, S., Toussaint, M., and Sebastian, T · 2018
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Learning unknown ODE models with Gaussian processes
Heinonen, M., Yildiz, C., Mannerström, H., Intosalmi, J., and Lähdesmäki, H · 2018
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Neural jump ordinary differential equations: Consistent continuous-time prediction and filtering
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Neural controlled differential equations for irregular time series
Kidger, P., Morrill, J., Foster, J., and Lyons, T · 2020
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Deep rao-blackwellised particle filters for time series forecasting
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Scalable gradients for stochastic differential equations
Li, X., Wong, T.-K. L., Chen, R. T., and Duvenaud, D · 2020
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Deep state space models for time series forecasting
Rangapuram, S. S., Seeger, M. W., Gasthaus, J., Stella, L., Wang, Y., and Januschowski, T · 2018
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Deep explicit duration switching models for time series
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Efficiently modeling long sequences with structured state spaces
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Latent matters: Learning deep state-space models
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Effectively modeling time series with simple discrete state spaces
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