Learning Probabilistic Symmetrization for Architecture Agnostic Equivariance
Authors: Jinwoo Kim, Dat Nguyen, Ayhan Suleymanzade, Hyeokjun An, Seunghoon Hong
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | 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]. |