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Methods based on ordinary differential equations (ODEs) are widely used to build generative models of time-series.
Digital signal processing(book)
Oppenheim, A. V. and Schafer, R. W · 1975
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Linear system theory and design
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Learning internal representations by error propagation
Rumelhart, D. E., Hinton, G. E., and Williams, R. J · 1985
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Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series
Peng, C.-K., Havlin, S., Stanley, H. E., and Goldberger, A. L · 1995
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Solving ordinary differential equations II , volume 375
Wanner, G. and Hairer, E · 1996
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The vanishing gradient problem during learning recurrent neural nets and problem solutions
Hochreiter, S · 1998
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Discrete-time signal processing
Oppenheim, A. V · 1999
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On the global error of discretization methods for ordinary differential equations
Niesen, J. and Hall, T · 2004
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Stiff systems
Shampine, L. F. and Thompson, S · 2007
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Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012
Silva, I., Moody, G., Scott, D. J., Celi, L. A., and Mark, R. G · 2012
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
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Learning stochastic recurrent networks
Bayer, J. and Osendorfer, C · 2014
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Empirical evaluation of gated recurrent neural networks on sequence modeling
Chung, J., Gulcehre, C., Cho, K., and Bengio, Y · 2014
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Variational recurrent auto-encoders
Fabius, O. and Van Amersfoort, J. R · 2014
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Importance weighted autoencoders
Burda, Y., Grosse, R., and Salakhutdinov, R · 2015
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A recurrent latent variable model for sequential data
Chung, J., Kastner, K., Dinh, L., Goel, K., Courville, A. C., and Bengio, Y · 2015
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Long-term daily climate records from stations across the contiguous united states, 2015
Menne, M., Williams Jr, C., and Vose, R · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S · 2015
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Wavenet: A generative model for raw audio
Oord, A. v. d., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., and Kavukcuoglu, K · 2016
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Seqgan: Sequence generative adversarial nets with policy gradient
Yu, L., Zhang, W., Wang, J., and Yu, Y · 2017
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Neural ordinary differential equations
Chen, R. T., Rubanova, Y., Bettencourt, J., and Duvenaud, D. K · 2018
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Gru-ode-bayes: Continuous modeling of sporadically-observed time series
De Brouwer, E., Simm, J., Arany, A., and Moreau, Y · 2019
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Poli, M., Park, J., and Ilievski, I · 2019
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Latent ordinary differential equations for irregularly-sampled time series
Rubanova, Y., Chen, R. T., and Duvenaud, D. K · 2019
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Generative modeling by estimating gradients of the data distribution
Dissecting neural odes
Massaroli, S., Poli, M., Park, J., Yamashita, A., and Asama, H · 2020
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Conditional sig-wasserstein gans for time series generation
Ni, H., Szpruch, L., Wiese, M., Liao, S., and Xiao, B · 2020
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Score-based generative modeling through stochastic differential equations
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., and Poole, B · 2020
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Adaptive checkpoint adjoint method for gradient estimation in neural ode
Zhuang, J., Dvornek, N., Li, X., Tatikonda, S., Papademetris, X., and Duncan, J · 2020
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Parallelizing legendre memory unit training
Chilkuri, N. R. and Eliasmith, C · 2021
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Song, Y. and Ermon, S · 2019
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Ode2vae: Deep generative second order odes with bayesian neural networks
Yildiz, C., Heinonen, M., and Lahdesmaki, H · 2019
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Time-series generative adversarial networks
Yoon, J., Jarrett, D., and Van der Schaar, M · 2019
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Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
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Normalizing kalman filters for multivariate time series analysis
de Bézenac, E., Rangapuram, S. S., Benidis, K., Bohlke-Schneider, M., Kurle, R., Stella, L., Hasson, H., Gallinari, P., and Januschowski, T · 2020
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Modeling continuous stochastic processes with dynamic normalizing flows
Deng, R., Chang, B., Brubaker, M. A., Mori, G., and Lehrmann, A · 2020
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Gp-vae: Deep probabilistic time series imputation
Fortuin, V., Baranchuk, D., Rätsch, G., and Mandt, S · 2020
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Godahewa, R., Bergmeir, C., Webb, G. I., Hyndman, R. J., and Montero-Manso, P · 2021
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Efficiently modeling long sequences with structured state spaces
Gu, A., Goel, K., and Ré, C · 2021
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Neural sdes as infinite-dimensional gans
Kidger, P., Foster, J., Li, X., and Lyons, T. J · 2021
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Stiff neural ordinary differential equations
Kim, S., Ji, W., Deng, S., Ma, Y., and Rackauckas, C · 2021
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Differentiable multiple shooting layers
Massaroli, S., Poli, M., Sonoda, S., Suzuki, T., Park, J., Yamashita, A., and Asama, H · 2021
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Autoregressive denoising diffusion models for multivariate probabilistic time series forecasting
Rasul, K., Seward, C., Schuster, I., and Vollgraf, R · 2021
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Ckconv: Continuous kernel convolution for sequential data
Romero, D. W., Kuzina, A., Bekkers, E. J., Tomczak, J. M., and Hoogendoorn, M · 2021
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Csdi: Conditional score-based diffusion models for probabilistic time series imputation
Tashiro, Y., Song, J., Song, Y., and Ermon, S · 2021
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Generating long videos of dynamic scenes
Brooks, T., Hellsten, J., Aittala, M., Wang, T.-C., Aila, T., Lehtinen, J., Liu, M.-Y., Efros, A. A., and Karras, T · 2022
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It’s raw! audio generation with state-space models
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On the parameterization and initialization of diagonal state space models
Gu, A., Gupta, A., Goel, K., and Ré, C · 2022
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Modeling irregular time series with continuous recurrent units
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Generating videos with dynamics-aware implicit generative adversarial networks
Yu, S., Tack, J., Mo, S., Kim, H., Kim, J., Ha, J.-W., and Shin, J · 2022
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