Transport of Algebraic Structure to Latent Embeddings
Authors: Samuel Pfrommer, Brendon G. Anderson, Somayeh Sojoudi
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate these structural transport nets for a range of mirrored algebras against baselines that operate directly on the latent space. Our experiments provide strong evidence that respecting the underlying algebraic structure of the input space is key for learning accurate and self-consistent operations. |
| Researcher Affiliation | Academia | 1University of California, Berkeley. Correspondence to: Samuel Pfrommer <sam.pfrommer@berkeley.edu>. |
| Pseudocode | Yes | Algorithm 1 Transport of algebraic structure from S to L |
| Open Source Code | Yes | Source code for our experiments is available on Git Hub. |
| Open Datasets | No | The paper mentions generating a synthetic random dataset and training/validation/testing splits from it but does not provide concrete access information (link, DOI, citation) to make this dataset publicly available or reproducible. |
| Dataset Splits | Yes | We generate 10^4 such random sets with an 80% training, 10% validation, and 10% testing split. |
| Hardware Specification | Yes | Reproducing these experiments takes approximately 5 GPU-days on an RTX A6000 GPU with an i7 core CPU. |
| Software Dependencies | Yes | Our codebase was developed against Py Torch 2.1.2. |
| Experiment Setup | Yes | Training runs for 10 epochs using Adam with a learning rate of 10^-3. ... Training uses the Adam optimizer with a learning rate of 0.01 and batch size of 10^3 for 10 epochs. ... We use the Adam optimizer for 50 epochs with a learning rate of 10^-4, weight decay of 10^-3, and batch size of 16. |