CoNSoLe: Convex Neural Symbolic Learning
Authors: Haoran Li, Yang Weng, Hanghang Tong
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we demonstrate the superior performance of the CONSOLE framework over the state-of-the-art on a diverse set of datasets. 1 Introduction ...5 Experiments ...5.1 Settings ...5.2 Verification and 3-D Visualization of Convex Mechanisms ...5.3 Convexity Guarantees of CONSOLE to Learn Correct Equations ...5.4 Ablation Study: Exploration and Convex Search are Essential ...5.5 CONSOLE is Robust with Changing Noise Levels and Data Volume |
| Researcher Affiliation | Academia | Haoran Li Yang Weng Arizona State University Tempe, AZ, 85287 {lhaoran, yang.weng}@asu.edu Hanghang Tong University of Illinois Urbana-Champaign Champaign, IL, 61820 htong@illinois.edu |
| Pseudocode | Yes | The overview of our framework is in Appendix A.1, Algorithm 1. The specific algorithm is in Appendix A.2, Algorithm 2. |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper describes creating its own synthetic datasets (Syn1, Syn2) and generating data through simulations using MATPOWER [39] for the Pow dataset and MATLAB for the Mas dataset. No concrete access information for publicly available datasets is provided. |
| Dataset Splits | No | The paper describes training and testing data splits (e.g., '2,000 samples for training... another 2,000 samples... for test'; 'first 8,760 points are used for training while the remaining samples are used for testing'), but does not explicitly mention a separate validation dataset split. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'MATPOWER' and 'MATLAB' for simulations, but does not provide specific version numbers for these or any other software dependencies required to replicate the experiments. |
| Experiment Setup | Yes | Finally, the hyper-parameter settings can be seen in Appendix A.7. |