R. T. Chen, Y. Rubanova, J. Bettencourt, and D. K. Duvenaud. Neural ordinary differential equations. Advances in neural information processing systems, 31, 2018.
R. T. Q. Chen. torchdiffreq, 2018. URL https://github.com/rtqichen/torchdiffreq.
E. De Brouwer, J. Simm, A. Arany, and Y. Moreau. Gru-ode-bayes: Continuous modeling of sporadically-observed time series. Advances in neural information processing systems, 32, 2019.
E. Dupont, A. Doucet, and Y. W. Teh. Augmented neural ODEs. In Advances in Neural Information Processing Systems, 2019.
C. Eastwood and C. K. Williams. A framework for the quantitative evaluation of disentangled representations. In International Conference on Learning Representations, 2018.
J.-Y. Franceschi, E. Delasalles, M. Chen, S. Lamprier, and P. Gallinari. Stochastic latent residual video prediction. In International Conference on Machine Learning, pages 3233-3246. PMLR, 2020.
Z. Gan, C. Li, R. Henao, D. E. Carlson, and L. Carin. Deep temporal sigmoid belief networks for sequence modeling. Advances in Neural Information Processing Systems, 28, 2015.
J.-B. Grill, F. Strub, F. Altché, C. Tallec, P. Richemond, E. Buchatskaya, C. Doersch, B. Avila Pires, Z. Guo, M. Gheshlaghi Azar, et al. Bootstrap your own latent-a new approach to self-supervised learning. Advances in neural information processing systems, 33:21271-21284, 2020.
P. Hegde, C. Yildiz, H. Lähdesmäki, S. Kaski, and M. Heinonen. Variational multiple shooting for bayesian odes with gaussian processes. In Uncertainty in Artificial Intelligence, pages 790-799. PMLR, 2022.
M. W. Hirsch, S. Smale, and R. L. Devaney. Differential equations, dynamical systems, and an introduction to chaos. Academic press, 2012.
V. Iakovlev, C. Yildiz, M. Heinonen, and H. Lähdesmäki. Latent neural ODEs with sparse bayesian multiple shooting. In The Eleventh International Conference on Learning Representations, 2023. URL https://openreview.net/forum?id=moI1FZfj_1b.
R. Kabra, D. Zoran, G. Erdogan, L. Matthew, A. Creswell, M. Botvinick, A. Lerchner, and C. Burgess. Simone: View-invariant, temporally-abstracted object representations via unsupervised video decomposition. Advances in Neural Information Processing Systems, 34:20146-20159, 2021.
D. Kanaa, V. Voleti, S. E. Kahou, and C. Pal. Simple video generation using neural odes. arXiv preprint arXiv:2109.03292, 2021.
P. Kidger, J. Morrill, J. Foster, and T. Lyons. Neural controlled differential equations for irregular time series. Advances in Neural Information Processing Systems, 33:6696-6707, 2020.
D. P. Kingma and J. Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.
D. P. Kingma and M. Welling. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013.
I. R. Kondor. Group theoretical methods in machine learning. Columbia University, 2008.
M. Lutter, C. Ritter, and J. Peters. Deep lagrangian networks: Using physics as model prior for deep learning. arXiv preprint arXiv:1907.04490, 2019.
A. Norcliffe, C. Bodnar, B. Day, N. Simidjievski, and P. Lio. On second order behaviour in augmented neural odes. Advances in neural information processing systems, 33:5911-5921, 2020.
A. Norcliffe, C. Bodnar, B. Day, J. Moss, and P. Lio. Neural ode processes. *ICLR*, 2021.
S. Park, K. Kim, J. Lee, J. Choo, J. Lee, S. Kim, and E. Choi. Vid-ode: Continuous-time video generation with neural ordinary differential equation. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 2412–2422, 2021.