• Corpus ID: 240070284

Disentangled generative models for robust dynamical system prediction

@inproceedings{Fotiadis2021DisentangledGM,
  title={Disentangled generative models for robust dynamical system prediction},
  author={Stathi Fotiadis and Shunlong Hu and Mario Lino and Chris D. Cantwell and Anil Anthony Bharath},
  year={2021}
}
Deep neural networks have become increasingly of interest in dynamical system prediction, but out-of-distribution generalization and long-term stability still remains challenging. In this work, we treat the domain parameters of dynamical systems as factors of variation of the data generating process. By leveraging ideas from supervised disentanglement and causal factorization, we aim to separate the domain parameters from the dynamics in the latent space of generative models. In our experiments… 

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