Inverse Learning of Symmetries
Authors: Mario Wieser, Sonali Parbhoo, Aleksander Wieczorek, Volker Roth
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that our model outperforms state-of-the-art methods on artificial and molecular datasets. Experiments on an artificial as well as two molecular datasets demonstrate that the proposed model learns both pre-defined and arbitrary symmetry transformations and outperforms state-of-the-art methods. |
| Researcher Affiliation | Academia | 1University of Basel, Switzerland 2National Centre for Computational Design and Discovery of Novel Materials MARVEL, University of Basel, Switzerland 3John A. Paulson School of Engineering and Applied Sciences, Harvard University, USA |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is publicly available at https://github.com/bmda-unibas/Inverse Learning Of Symmetries |
| Open Datasets | Yes | We use the 134K organic molecules from the QM9 database [31], which consists of up to nine main group atoms (C, O, N and F), not counting hydrogens. |
| Dataset Splits | No | The paper mentions 'training and testing phase' for the artificial dataset and uses the QM9 dataset, but it does not provide specific numerical percentages or counts for training, validation, and test splits, nor does it specify cross-validation methods or reference predefined splits with sufficient detail for reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details (such as exact GPU/CPU models or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions various models and techniques but does not specify any software dependencies with version numbers (e.g., specific deep learning frameworks like PyTorch or TensorFlow with their versions). |
| Experiment Setup | No | The paper states 'A description of setups and additional experiments can be found in the supplementary materials' but does not provide specific experimental setup details such as hyperparameters or training configurations in the main text. |