Learning a Gradient-free Riemannian Optimizer on Tangent Spaces
Authors: Xiaomeng Fan, Zhi Gao, Yuwei Wu, Yunde Jia, Mehrtash Harandi7377-7384
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically show that our method brings efficient learning of the optimizer, while enjoying a good optimization trajectory in a data-driven manner.In this section, we evaluate our method from three perspectives, that is, the convergence, accuracy and efficiency. |
| Researcher Affiliation | Academia | 1 Beijing Laboratory of Intelligent Information Technology School of Computer Science, Beijing Institute of Technology, Beijing, China 2 Department of Electrical and Computer Systems Eng., Monash University, and Data61, Australia |
| Pseudocode | No | The paper does not include formal pseudocode or algorithm blocks. Figure 2 and Figure 3 depict architectural diagrams and computational graphs, not step-by-step algorithms. |
| Open Source Code | Yes | The code is available at https://github.com/Xiaomeng Fan-mcislab/Learning-a-Gradient-free-Riemannian-Optimizer-on-Tangent-Spaces |
| Open Datasets | Yes | We utilize the MNIST dataset to evaluate our optimizer.We use the Yale B dataset (Lee, Ho, and Kriegman 2005) to evaluate our optimizer.We evaluate our optimizer on the Kylberg texture dataset (Kylberg 2011). |
| Dataset Splits | No | The paper mentions training and testing but does not explicitly detail a separate validation split or how hyperparameters were tuned using a validation set. It states "hyperparameters of all optimizers are tuned to achieve the best performance" which implies validation, but no specifics are given. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as GPU models, CPU types, or memory specifications. It only mentions training times. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the implementation or experiments. |
| Experiment Setup | No | The paper states that "hyperparameters of all optimizers are tuned to achieve the best performance," but it does not provide specific values for these hyperparameters (e.g., learning rate, batch size, number of epochs) or detailed training configurations. |