Jul 1, 2019 · In this paper, we propose a novel latent dynamic model that is capable of capturing nonlinear, non-Markovian, long short-term time-dependent dynamics via ...

In this paper, we propose a novel la- tent dynamic model that is capable of captur- ing nonlinear, non-Markovian, long short-term time-dependent dynamics via ...

This paper proposes a novel latent dynamic model that is capable of capturing nonlinear, non-Markovian, long short-term time-dependent dynamics via ...

Duration: 15:07
Posted: Oct 23, 2019

Dec 15, 2020 · Bibliographic details on Neural Dynamics Discovery via Gaussian Process Recurrent Neural Networks.

We propose a novel latent dynamic model that is capable of capturing nonlinear, non-Markovian, long-short term time-dependent dynamics via recurrent neural ...

Advances in Neural Information Processing Systems 33, 6040-6052, 2020. 48, 2020. Neural dynamics discovery via gaussian process recurrent neural networks. Q She ...

NEURAL DYNAMICS DISCOVERY VIA GAUSSIAN PROCESS. RECURRENT NEURAL NETWORKS ... 10−2) of latent trajectory reconstruction using Gaussian process (GP-RNN) and neural ...

The model generates the latent representation via a Gaussian process prior or a Normal prior, and then maps the latent to the high-dimensional neural activity ...

Neural Dynamics Discovery via Gaussian Process Recurrent Neural Networks · 1 code implementation • 1 Jul 2019 • Qi She, Anqi Wu. In the experiment, we show ...