SEGP-VAE learns stable low-dimensional LTI systems from video data by deriving GP mean and covariance from LTI equations and using a complete unconstrained parametrization of semi-contracting systems.
Achiev- ing stable dynamics in neural circuits,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
The paper reviews and extends energy-based dynamical models that use gradient flows and energy landscapes for neurocomputation, learning, and optimization tasks.
citing papers explorer
-
Stability Enhanced Gaussian Process Variational Autoencoders
SEGP-VAE learns stable low-dimensional LTI systems from video data by deriving GP mean and covariance from LTI equations and using a complete unconstrained parametrization of semi-contracting systems.
-
Energy-Based Dynamical Models for Neurocomputation, Learning, and Optimization
The paper reviews and extends energy-based dynamical models that use gradient flows and energy landscapes for neurocomputation, learning, and optimization tasks.