Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Learning Probabilistic Symmetrization for Architecture Agnostic Equivariance
Authors: Jinwoo Kim, Dat Nguyen, Ayhan Suleymanzade, Hyeokjun An, Seunghoon Hong
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical tests show competitive results against tailored equivariant architectures, suggesting the potential for learning equivariant functions for diverse groups using a non-equivariant universal base architecture. We further show evidence of enhanced learning in symmetric modalities, like graphs, when pretrained from non-symmetric modalities, like vision. |
| Researcher Affiliation | Academia | Jinwoo Kim Tien Dat Nguyen Ayhan Suleymanzade Hyeokjun An Seunghoon Hong KAIST |
| Pseudocode | No | The paper describes methods and processes in text and mathematical equations, but it does not include any clearly labeled pseudocode blocks or algorithms. |
| Open Source Code | Yes | Code is available at https://github.com/jw9730/lps. |
| Open Datasets | Yes | For empirical demonstration, we adopt the experimental setup of [41] and use the n-body dataset [84, 31] where the task is predicting the position of n = 5 charged particles after certain time given their initial position and velocity in R3 (Sn E(3) equivariant). |
| Dataset Splits | No | The paper mentions datasets by name (e.g., 'GRAPH8c', 'EXP', 'n-body', 'PATTERN', 'Peptides-func', 'Peptides-struct', 'PCQM-Contact') and their general characteristics (Table 5 and 6), and uses validation for early stopping ('early stopping based on validation loss'), but it does not explicitly provide the exact train/validation/test split percentages or sample counts for all datasets. |
| Hardware Specification | Yes | which takes around 30 minutes on a single RTX 3090 GPU with 24GB using Py Torch [72]. |
| Software Dependencies | Yes | which takes around 30 minutes on a single RTX 3090 GPU with 24GB using Py Torch [72]. |
| Experiment Setup | Yes | we train our models with binary cross-entropy loss using Adam optimizer [46] with batch size 100 and learning rate 1e-3 for 2,000 epochs, which takes around 30 minutes on a single RTX 3090 GPU with 24GB using Py Torch [72]. |