Identification of Gaussian Process State Space Models

30 May 2017

Stefanos Eleftheriadis

Papers citing "Identification of Gaussian Process State Space Models"

23 / 23 papers shown

Title
Efficient Transformed Gaussian Process State-Space Models for Non-Stationary High-Dimensional Dynamical Systems Zhidi Lin Ying Li Feng Yin Juan Maroñas Alexandre Thiéry 54 0 0 24 Mar 2025
Modeling Latent Neural Dynamics with Gaussian Process Switching Linear Dynamical Systems Amber Hu D. Zoltowski Aditya Nair David Anderson Lea Duncker Scott W. Linderman 36 3 0 19 Jul 2024
Future Aware Safe Active Learning of Time Varying Systems using Gaussian Processes Markus Lange-Hegermann Christoph Zimmer AI4TS 47 0 0 17 May 2024
Multi-Response Heteroscedastic Gaussian Process Models and Their Inference Taehee Lee Jun S. Liu 13 1 0 29 Aug 2023
Free-Form Variational Inference for Gaussian Process State-Space Models Xuhui Fan Edwin V. Bonilla T. O’Kane Scott A. Sisson 16 9 0 20 Feb 2023
Sequential Estimation of Gaussian Process-based Deep State-Space Models Yuhao Liu Marzieh Ajirak P. Djuric 26 12 0 29 Jan 2023
Towards Flexibility and Interpretability of Gaussian Process State-Space Model Zhidi Lin Feng Yin Juan Maroñas 34 7 0 21 Jan 2023
On Uncertainty in Deep State Space Models for Model-Based Reinforcement Learning P. Becker Gerhard Neumann 30 9 0 17 Oct 2022
Variational Inference for Model-Free and Model-Based Reinforcement Learning Felix Leibfried OffRL 20 0 0 04 Sep 2022
SSDNet: State Space Decomposition Neural Network for Time Series Forecasting Yang Lin I. Koprinska Mashud Rana BDL AI4TS 26 31 0 19 Dec 2021
On the Stochastic Stability of Deep Markov Models Ján Drgoňa Sayak Mukherjee Jiaxin Zhang Frank Liu M. Halappanavar BDL 25 5 0 08 Nov 2021
Variational multiple shooting for Bayesian ODEs with Gaussian processes Pashupati Hegde Çağatay Yıldız Harri Lähdesmäki Samuel Kaski Markus Heinonen 30 16 0 21 Jun 2021
Safe Chance Constrained Reinforcement Learning for Batch Process Control M. Mowbray Panagiotis Petsagkourakis Ehecatl Antonio del Rio Chanona Dongda Zhang OffRL 34 34 0 23 Apr 2021
A Tutorial on Sparse Gaussian Processes and Variational Inference Felix Leibfried Vincent Dutordoir S. T. John N. Durrande GP 42 49 0 27 Dec 2020
Learning Constrained Dynamics with Gauss Principle adhering Gaussian Processes A. R. Geist Sebastian Trimpe 25 21 0 23 Apr 2020
Recurrent Attentive Neural Process for Sequential Data Shenghao Qin Jiacheng Zhu Jimmy Qin Wenshuo Wang Ding Zhao BDL AI4TS 27 38 0 17 Oct 2019
Learning GPLVM with arbitrary kernels using the unscented transformation Daniel Augusto R. M. A. de Souza Diego Mesquita C. L. C. Mattos Joao P. P. Gomes 29 0 0 03 Jul 2019
Neural Dynamics Discovery via Gaussian Process Recurrent Neural Networks Qi She Anqi Wu BDL 23 34 0 01 Jul 2019
Sequential Neural Processes Gautam Singh Jaesik Yoon Youngsung Son Sungjin Ahn BDL AI4TS 40 81 0 24 Jun 2019
Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models Alessandro Davide Ialongo Mark van der Wilk J. Hensman C. Rasmussen 38 30 0 13 Jun 2019
Learning Nonlinear State Space Models with Hamiltonian Sequential Monte Carlo Sampler Duo Xu 23 2 0 03 Jan 2019
Continuous time Gaussian process dynamical models in gene regulatory network inference A. Aalto L. Viitasaari Pauliina Ilmonen Laurent Mombaerts Jorge M. Gonçalves 16 7 0 24 Aug 2018
Manifold Gaussian Processes for Regression Roberto Calandra Jan Peters C. Rasmussen M. Deisenroth 92 271 0 24 Feb 2014