Latent Space Energy-based Neural ODEs
<|reference_start|>Latent Space Energy-based Neural ODEs: This paper introduces a novel family of deep dynamical models designed to represent continuous-time sequence data. This family of models generates each data point in the time series by a neural emission model, which is a non-linear transformation of a latent state vector. The trajectory of the latent states is implicitly described by a neural ordinary differential equation (ODE), with the initial state following an informative prior distribution parameterized by an energy-based model. Furthermore, we can extend this model to disentangle dynamic states from underlying static factors of variation, represented as time-invariant variables in the latent space. We train the model using maximum likelihood estimation with Markov chain Monte Carlo (MCMC) in an end-to-end manner, without requiring additional assisting components such as an inference network. Our experiments on oscillating systems, videos and real-world state sequences (MuJoCo) illustrate that ODEs with the learnable energy-based prior outperform existing counterparts, and can generalize to new dynamic parameterization, enabling long-horizon predictions.<|reference_end|>
@article{cheng2024latent,
    title={Latent Space Energy-based Neural ODEs},
    author={Sheng Cheng, Deqian Kong, Jianwen Xie, Kookjin Lee, Ying Nian Wu,
  Yezhou Yang},
    journal={arXiv preprint arXiv:2409.03845},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.03845},
    primaryClass={cs.LG stat.ML}
}