Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
LieGG: Studying Learned Lie Group Generators
Authors: Artem Moskalev, Anna Sepliarskaia, Ivan Sosnovik, Arnold Smeulders
NeurIPS 2022 | Venue PDF | LLM Run Details | Input Tokens: 15,736 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,518 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experiments Firstly, we conduct the experiment on the synthetic problem to test the ability of our method to accurately retrieve a symmetry learned by a network. To do so, we adopt the invariant regression task from [14] with clean O(5) symmetry built-in. |
| Researcher Affiliation | Collaboration | Artem Moskalev Uv A-Bosch Delta Lab University of Amsterdam EMAIL Anna Sepliarskaia Machine Learning Research Unit TU Wien EMAIL Ivan Sosnovik Uv A-Bosch Delta Lab University of Amsterdam EMAIL Arnold Smeulders Uv A-Bosch Delta Lab University of Amsterdam EMAIL |
| Pseudocode | No | The paper describes steps and calculations in text, but it does not contain structured pseudocode or algorithm blocks with formal labels. |
| Open Source Code | Yes | Source code: https://github.com/amoskalev/liegg |
| Open Datasets | Yes | In this experiment, we test the ability of our method to retrieve learned symmetries on the rotation MNIST dataset [23]. |
| Dataset Splits | Yes | Once the network s performance plateaus on the validation split, we terminate the training, and apply our method to retrieve learned symmetries. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python version, library versions like PyTorch or TensorFlow). |
| Experiment Setup | No | The paper discusses varying network architectures (number of parameters from [40000, 200000] and depth from 1 to 5 hidden layers) and training until validation convergence. However, it does not explicitly state specific hyperparameter values like learning rate, batch size, or optimizer settings for reproducibility. |