Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models
Authors: Alessandro Davide Ialongo, Mark Van Der Wilk, James Hensman, Carl Edward Rasmussen
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In the experiments we set out to test how the proposed posterior (VCDT) compares with the Factorised linear, Factorised non-linear, and PR-SSM (Doerr et al., 2018) approximations. To answer these questions, we test model calibration and predictive performance in a number of environments: the kink function, a set of five system identification benchmark datasets (also used in (Doerr et al., 2018)), and a cart and pole system. |
| Researcher Affiliation | Collaboration | 1Computational and Biological Learning Group, University of Cambridge 2Max Planck Institute for Intelligent Systems, T ubingen 3PROWLER.io. Correspondence to: Alessandro Davide Ialongo <adi24@cam.ac.uk>. |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | Yes | Code is available at: github.com/ialong/GPt. |
| Open Datasets | Yes | We also train our models on five benchmark datasets taken from (De Moor et al., 1997). Test set results are reported in table 2. |
| Dataset Splits | No | The paper states 'we train on the first half of the sequence' and discusses 'Test set results', but no explicit mention of a validation set or its split information is provided. |
| Hardware Specification | No | No specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running experiments were mentioned. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., library or solver names with versions) were explicitly stated. |
| Experiment Setup | Yes | No mini-batching was used, the bound is evaluated using 100 samples from the posterior, and we use 100 variational inducing points. Results for models fit using 100 samples from the posterior (per optimisation step) and 300 inducing points are shown in table 3. |