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Found 6 papers, 5 papers with code
Modulated Neural ODEs
Neural ordinary differential equations (NODEs) have been proven useful for learning non-linear dynamics of arbitrary trajectories.
Learning Interacting Dynamical Systems with Latent Gaussian Process ODEs
We study time uncertainty-aware modeling of continuous-time dynamics of interacting objects.
Variational multiple shooting for Bayesian ODEs with Gaussian processes
Recent machine learning advances have proposed black-box estimation of unknown continuous-time system dynamics directly from data.
Continuous-Time Model-Based Reinforcement Learning
Model-based reinforcement learning (MBRL) approaches rely on discrete-time state transition models whereas physical systems and the vast majority of control tasks operate in continuous-time.
Model-based Reinforcement Learning
reinforcement-learning
+1
ODE$^2$VAE: Deep generative second order ODEs with Bayesian neural networks
We present Ordinary Differential Equation Variational Auto-Encoder (ODE$^2$VAE), a latent second order ODE model for high-dimensional sequential data.
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Asynchronous Stochastic Quasi-Newton MCMC for Non-Convex Optimization
The results support our theory and show that the proposed algorithm provides a significant speedup over the recently proposed synchronous distributed L-BFGS algorithm.
