no code implementations • 9 Jun 2020 • Baocheng Zhu, Shijun Wang, James Zhang
In this paper, by leveraging recent progresses in representation learning and state space models (SSMs), we propose NeurPhy, which uses variational auto-encoder (VAE) to extract underlying Markovian dynamic state at each time step, neural process (NP) to extract the global system parameters, and a non-linear non-recurrent stochastic state space model to learn the physical dynamic transition.
no code implementations • 19 May 2020 • Shijun Wang, Baocheng Zhu, Lintao Ma, Yuan Qi
In this paper, we consider optimizing a smooth, convex, lower semicontinuous function in Riemannian space with constraints.
no code implementations • 19 May 2020 • Shijun Wang, Baocheng Zhu, Chen Li, Mingzhe Wu, James Zhang, Wei Chu, Yuan Qi
In this paper, We propose a general Riemannian proximal optimization algorithm with guaranteed convergence to solve Markov decision process (MDP) problems.