Automated Symbolic Law Discovery: A Computer Vision Approach
Authors: Hengrui Xing, Ansaf Salleb-Aouissi, Nakul Verma660-668
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply our model to a variety of plausible relationships both simulated and from physics and mathematics domains involving different dimensions and constituents. We show that our model is able to identify the underlying operators from data, achieving a high accuracy and AUC (91% and 0.96 on average resp.) for systems with as many as ten independent variables. Our method significantly outperforms the current state of the art in terms of data fitting (R2), discovery rate (recovering the true relationship), and succinctness (output formula complexity). |
| Researcher Affiliation | Academia | Hengrui Xing, Ansaf Salleb-Aouissi, Nakul Verma Department of Computer Science, Columbia University, New York, USA h.xing@columbia.edu, ansafsalleb@columbia.edu, verma@cs.columbia.edu |
| Pseudocode | No | The paper describes its methodology but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions using the GPLearn software package and provides its URL (1Stephens, T. 2018. GPLearn: Genetic Programming in Python. URL: https://github.com/trevorstephens/gplearn), but it does not state that the authors' own code for De STr OI is publicly available. |
| Open Datasets | No | The paper describes generating a 'synthetic dataset' for training and testing, but it does not provide concrete access information (link, DOI, repository, or formal citation) for this dataset or any other publicly available dataset used for training. |
| Dataset Splits | Yes | We randomly split the dataset into 80% for training, 10% for early-stopping, and 10% for testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (such as GPU or CPU models, processor types, or memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions using the 'GPLearn software package' but does not provide a specific version number for it or any other software dependencies like programming languages or libraries. |
| Experiment Setup | Yes | We regularize the model with an L2 weight decay and 50% dropouts for the fully connected layers. |