Scalable Variational Inference for Dynamical Systems
Authors: Nico S. Gorbach, Stefan Bauer, Joachim M. Buhmann
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In order to provide a fair comparison to existing approaches, we test our approach on two small to medium sized ODE models, which have been extensively studied in the same parameter settings before [e.g. Calderhead et al., 2008, Dondelinger et al., 2013, Wang and Barber, 2014]. Additionally, we show the scalability of our approach on a large-scale partially observable system which has so far been infeasible to analyze with existing gradient matching methods due to the number of unobserved states. |
| Researcher Affiliation | Academia | Nico S. Gorbach Dept. of Computer Science ETH Zurich ngorbach@inf.ethz.ch Stefan Bauer Dept. of Computer Science ETH Zurich bauers@inf.ethz.ch Joachim M. Buhmann Dept. of Computer Science ETH Zurich jbuhmann@inf.ethz.ch |
| Pseudocode | Yes | Algorithm 1 Mean-field coordinate ascent for GP Gradient Matching |
| Open Source Code | No | The paper does not provide an explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The data used for experiments is simulated, not from a publicly available dataset. For example: 'We used the same ODE parameters as in Dondelinger et al. [2013]...to simulate the data...Mean-field variational inference for gradient matching was performed on a simulated dataset with additive Gaussian noise with variance σ2 = 0.25.' |
| Dataset Splits | No | The paper does not specify training, validation, or test dataset splits. The data used in the experiments is simulated based on given parameters or time points, not partitioned from a larger corpus. |
| Hardware Specification | Yes | All experiments were run on a 2.5 GHz Intel Core i7 Macbook. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers for reproducibility (e.g., 'Python 3.8', 'PyTorch 1.9'). |
| Experiment Setup | Yes | We used the same ODE parameters as in Dondelinger et al. [2013] (i.e. θ1 = 2, θ2 = 1, θ3 = 4, θ4 = 1) to simulate the data over an interval [0, 2] with a sampling interval of 0.1. Predator species (i.e. x1) were initialized to 3 and prey species (i.e. x) were initialized to 5. Mean-field variational inference for gradient matching was performed on a simulated dataset with additive Gaussian noise with variance σ2 = 0.25. |