Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Adaptive Path-Integral Autoencoders: Representation Learning and Planning for Dynamical Systems
Authors: Jung-Su Ha, Young-Jin Park, Hyeok-Joo Chae, Soon-Seo Park, Han-Lim Choi
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments show that the proposed path-integral control based variational inference method leads to tighter lower bounds in statistical model learning of sequential data. |
| Researcher Affiliation | Academia | Jung-Su Ha, Young-Jin Park, Hyeok-Joo Chae, Soon-Seo Park, and Han-Lim Choi Department of Aerospace Engineering & KI for Robotics, KAIST Daejeon 305-701, Republic of Korea {{jsha, yjpark, hjchae, sspark}@lics., hanlimc@}kaist.ac.kr |
| Pseudocode | Yes | We implemented APIAE with Tensorflow [Abadi et al., 2016]; the pseudo code and algorithmic details of APIAE are given in the Appendix C. |
| Open Source Code | Yes | The supplementary video1 and the implementation code2 are available online. 2https://github.com/yjparkLiCS/18-NeurIPS-APIAE |
| Open Datasets | Yes | We utilized human motion capture data from the Carnegie Mellon University motion capture (CMU mocap) database for the learning; |
| Dataset Splits | No | The paper specifies training and test data amounts but does not mention a separate validation split for either experiment (e.g., Pendulum: '3000 and 500 data are used for training and test, respectively.' Mocap: '1043 and 173 data are used for training and test, respectively.'). |
| Hardware Specification | No | The paper mentions that 'the whole operations were parallelized with GPU' but does not specify any particular GPU model, CPU, or other hardware components. |
| Software Dependencies | No | The paper mentions 'We implemented APIAE with Tensorflow [Abadi et al., 2016]' but does not provide a specific version number for TensorFlow or any other software. |
| Experiment Setup | Yes | We set our APIAE parameters as L=8, R=4, and K=10 during experiments. |