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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Symmetry Teleportation for Accelerated Optimization
Authors: Bo Zhao, Nima Dehmamy, Robin Walters, Rose Yu
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimentally, we show that teleportation improves the convergence speed of gradient descent and Ada Grad for several optimization problems including test functions, multi-layer regressions, and MNIST classification. |
| Researcher Affiliation | Collaboration | Bo Zhao University of California, San Diego EMAIL Nima Dehmamy IBM Research EMAIL Robin Walters Northeastern University EMAIL Rose Yu University of California, San Diego EMAIL |
| Pseudocode | Yes | Algorithm 1: Symmetry Teleportation |
| Open Source Code | Yes | Our code is available at https://github.com/Rose-STL-Lab/Symmetry-Teleportation. |
| Open Datasets | Yes | MNIST classification. We apply symmetry teleportation on the MNIST classification task (Deng, 2012). |
| Dataset Splits | Yes | We split the training set into 48,000 for training and 12,000 for validation. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware used (e.g., GPU/CPU models, memory) to run the experiments in the main text. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | Rosenbrock function: "Each algorithm is run 1000 steps with learning rate 10 3. We teleport the parameters every 100 steps." MNIST classification: "Learning rate is 2 10 3, and learning rate for teleportation is 10 3. Each optimization algorithm is run 80 epochs with batch size of 20." |