Code and data associated with the following publications: Velychko, D., Knopp, B. and Endres D. (2017). The coupled variational Gaussian process dynamical model. In Proceedings of the 27 International Conference on Artificial Neural Networks,pages 1–9. Velychko, D., Knopp, B. and Endres D. (2016). The variational coupled Gaussian process dynamical model (Abstract). NIPS Workshop on Neurorobotics. The zip file contains training data ('walking_movement.bvh') and code to train vCGPDMs and produce artificial movements which were used as stimuli for the psychophysical experiment. Additional information is provided in the README file
The code provided here originally demonstrated the main algorithms from Rasmussen and Williams: Gaus...
The GD-VAE package provides data-driven methods for learning representations of system states and no...
This package contains the raw data / logs (fetched from WandB) for the experiments of the following ...
We describe a sparse, variational posterior approximation to the Coupled Gaussian Process Dynamical ...
Contains fulltext : 205110.pdf (publisher's version ) (Open Access)Radboud Univers...
This paper introduces Gaussian Process Dynamical Models (GPDM) for nonlinear time series analysis. A...
High dimensional time series are endemic in applications of machine learning such as robotics (senso...
These datasets are used to reproduce the results of the neural dynamics model from the paper https:/...
We develop data-driven methods incorporating geometric and topological information to learn parsimon...
Falsification is the basis for testing existing hypotheses, and a great danger is posed when results...
Generating training data for identifying neurofuzzy models of non-linear dynamic systems This item w...
This paper introduces Gaussian Process Dynamical Models (GPDM) for nonlinear time series analysis. A...
Falsification is the basis for testing existing hypotheses, and a great danger is posed when results...
Supplementary data and R/Python code required to reproduce the figures from the accompanying publica...
This repository contains all the neural and simulated datasets used in the above publication. All th...
The code provided here originally demonstrated the main algorithms from Rasmussen and Williams: Gaus...
The GD-VAE package provides data-driven methods for learning representations of system states and no...
This package contains the raw data / logs (fetched from WandB) for the experiments of the following ...
We describe a sparse, variational posterior approximation to the Coupled Gaussian Process Dynamical ...
Contains fulltext : 205110.pdf (publisher's version ) (Open Access)Radboud Univers...
This paper introduces Gaussian Process Dynamical Models (GPDM) for nonlinear time series analysis. A...
High dimensional time series are endemic in applications of machine learning such as robotics (senso...
These datasets are used to reproduce the results of the neural dynamics model from the paper https:/...
We develop data-driven methods incorporating geometric and topological information to learn parsimon...
Falsification is the basis for testing existing hypotheses, and a great danger is posed when results...
Generating training data for identifying neurofuzzy models of non-linear dynamic systems This item w...
This paper introduces Gaussian Process Dynamical Models (GPDM) for nonlinear time series analysis. A...
Falsification is the basis for testing existing hypotheses, and a great danger is posed when results...
Supplementary data and R/Python code required to reproduce the figures from the accompanying publica...
This repository contains all the neural and simulated datasets used in the above publication. All th...
The code provided here originally demonstrated the main algorithms from Rasmussen and Williams: Gaus...
The GD-VAE package provides data-driven methods for learning representations of system states and no...
This package contains the raw data / logs (fetched from WandB) for the experiments of the following ...