Neural Stochastic Control
Authors: Jingdong Zhang, Qunxi Zhu, Wei LIN
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we demonstrate the efficacy of the above-articulated frameworks of stochastic neural control, the ES and the AS, on several representative physical systems. We also investigate rigorously the linear controller and the proposed neural stochastic controllers in both convergence time and energy cost and numerically compare them in these two indexes. |
| Researcher Affiliation | Academia | School of Mathematical Sciences, SCMS, SCAM, and CCSB, Fudan University Research Institute of Intelligent Complex Systems and MOE Frontiers Center for Brain Science, Fudan University Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University State Key Laboratory of Medical Neurobiology, Institutes of Brain Science, Fudan University Shanghai Artificial Intelligence Laboratory |
| Pseudocode | Yes | For convenience, we summarize the developed framework in Algorithm 1. For convenience, we summarize the AS framework in Algorithm 2. |
| Open Source Code | Yes | and we make our code available at https://github.com/jingddong-zhang/Neural-Stochastic-Control. |
| Open Datasets | No | The paper uses physical systems like the Harmonic Linear Oscillator, Stuart-Landau Equations, and Cell Fate Dynamics. While these are described, no specific access information (link, DOI, repository, or formal citation with authors/year) is provided for the datasets or time series data used in the experiments to confirm public availability. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) for training, validation, or testing. |
| Hardware Specification | No | The main text of the paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. It defers this information to the supplementary material. |
| Software Dependencies | No | The paper mentions software like NNs and uses terms like 'PyTorch' in general context (not shown in provided excerpt), but the provided text does not include specific version numbers for key software components or libraries required for replication. |
| Experiment Setup | Yes | The detailed configurations for these experiments are included in Appendix A.5. Additional illustrative experiments are included in Appendix A.6. the detailed training configurations are shown in Appendix A.5.1. |