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..
Large Learning Rate Tames Homogeneity: Convergence and Balancing Effect
Authors: Yuqing Wang, Minshuo Chen, Tuo Zhao, Molei Tao
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments are provided to support our theory. |
| Researcher Affiliation | Academia | Yuqing Wang, Minshuo Chen, Tuo Zhao, Molei Tao Georgia Institute of Technology EMAIL |
| Pseudocode | No | The paper describes mathematical update rules for Gradient Descent but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper describes generating elements for matrix A from a Gaussian distribution and generating initial conditions (X0, Y0) randomly. It does not mention using any publicly available or open datasets with concrete access information. |
| Dataset Splits | No | The paper does not provide specific details about training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run its experiments, such as specific GPU or CPU models. |
| Software Dependencies | No | The paper mentions Gradient Descent (GD) but does not provide specific software dependencies with version numbers (e.g., programming languages, libraries, or frameworks). |
| Experiment Setup | Yes | The initial conditions are randomly generated, respectively with ( x0 , y0 ) = (9, 1), ( x0 , y0 ) = (19, 1), and ( x0 , y0 ) = (99, 1); the learning rates are chosen within the range of Theorem 3.1 from large to small as h0, 6 7h0 for the 1st-6th columns respectively where h0 = 4/( x0 2 + y0 2 + 8). |