Probabilistic Recurrent State-Space Models
Authors: Andreas Doerr, Christian Daniel, Martin Schiegg, Nguyen-Tuong Duy, Stefan Schaal, Marc Toussaint, Trimpe Sebastian
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The effectiveness of the proposed PR-SSM is evaluated on a set of real-world benchmark datasets in comparison to state-of-the-art probabilistic model learning methods. Scalability and robustness are demonstrated on a high dimensional problem. |
| Researcher Affiliation | Collaboration | 1Bosch Center for Artiļ¬cial Intelligence, Renningen, Germany. 2Max Planck Institute for Intelligent Systems, Stuttgart/T ubingen, Germany. 3University of Southern California, Los Angeles, USA. 4Machine Learning and Robotics Lab, University of Stuttgart, Germany. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | 1Code available at: https://github.com/boschresearch/PR-SSM . |
| Open Datasets | Yes | The performance of PR-SSM is assessed in comparison to state-of-the-art model learning methods on several real-world datasets as previously utilized by (Mattos et al., 2015). and Table 1 lists the datasets: ACTUATOR, BALLBEAM, DRIVES, FURNACE, DRYER, SARCOS. |
| Dataset Splits | No | The paper refers to 'test dataset' but does not specify the train/validation/test dataset splits (percentages or counts) needed to reproduce the experiment. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | In the experiments, 50 latent state samples were employed (details in the supplementary material). |