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..
Efficient Riemannian Meta-Optimization by Implicit Differentiation
Authors: Xiaomeng Fan, Yuwei Wu, Zhi Gao, Yunde Jia, Mehrtash Harandi3733-3740
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Evaluations of three optimization problems on different Riemannian manifolds show that our method achieves state-of-the-art performance in terms of the convergence speed and the quality of optima. Experiments were conducted on three tasks: principal component analysis (PCA) on the Grassmann manifold, face Recognition on the Stiefel Manifold, and clustering on the SPD manifold... |
| Researcher Affiliation | Collaboration | 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 | Yes | Algorithm 1 Parameter Warmup stage |
| Open Source Code | Yes | The code is available at https://github.com/XiaomengFanmcislab/I-RMM. |
| Open Datasets | Yes | We used MNIST dataset to evaluate our method on the PCA task. We utilized the Yale B dataset (Lee, Ho, and Kriegman 2005) to conduct this experiment. We also conducted experiments on the clustering task of SPD representations by utilizing the Kylberg texture dataset (Kylberg 2011). |
| Dataset Splits | Yes | LV (X(T )) is the loss function of the updated Riemannian parameter X(T ) on validation data. |
| Hardware Specification | No | The paper mentions 'GPU memory consumption' but does not provide specific details on the hardware used (e.g., GPU model, CPU, RAM). |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | Require: Initial optimization state S(0) = 0, initial parameters ϕ of our optimizer, maximum iteration T of the inner-loop, maximum iteration Υ of the outer-loop, and hyperparameter B to update the parameter pool. Table 1: Training time (seconds) comparisons on the PCA task (showing specific Inner Loop Steps values). |