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..
Online Learning and Control of Complex Dynamical Systems from Sensory Input
Authors: Oumayma Bounou, Jean Ponce, Justin Carpentier
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The proposed approach is evaluated empirically on several classical dynamical systems and sensory modalities, with good performance on long-term prediction and control. |
| Researcher Affiliation | Academia | 1Inria and Département d Informatique de l Ecole Normale Supérieure, PSL Research University 2Center for Data Science, New York University |
| Pseudocode | No | The paper does not contain explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | 3. (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] see supplemental material |
| Open Datasets | No | We generate three black-and-white 64 64 video datasets of classical dynamical systems using the Pinocchio library [49] for the simulation and Panda3D-viewer [50] for the rendering: a simple pendulum, a double pendulum1 and a cartpole. ... More details on the datasets generation can be found in the supplementary material. |
| Dataset Splits | No | The paper mentions "training datasets" and evaluation on sequences of different lengths (e.g., "trained on 5 s sequences" and "evaluated on 15 s sequences") but does not explicitly specify train/validation/test splits or mention a "validation" set. |
| Hardware Specification | Yes | Each learning problem takes about 3 hours to solve on an Nvidia RTX6000 GPU. |
| Software Dependencies | No | The paper mentions software like "Pinocchio library [49]", "Panda3D-viewer [50]", and "Py Torch" but does not specify their version numbers. |
| Experiment Setup | Yes | The dimension of the codes in latent space corresponds to the bottleneck size of the autoencoder, set to nz = 8 in all our experiments. More details on the architecture can be found in the supplementary material. The models of the unactuated systems are trained on 5 s sequences and evaluated on 15 s sequences, and those of actuated systems were trained on 10 s sequences and evaluated on 20 s sequences. |