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..
Acceleration via Fractal Learning Rate Schedules
Authors: Naman Agarwal, Surbhi Goel, Cyril Zhang
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide some experiments to challenge conventional beliefs about stable learning rates in deep learning: the fractal schedule enables training to converge with locally unstable updates which make negative progress on the objective. |
| Researcher Affiliation | Industry | 1Google AI Princeton, Princeton, NJ, USA 2Microsoft Research, New York, NY, USA. Correspondence to: Cyril Zhang <EMAIL>. |
| Pseudocode | No | The paper defines constructions and outlines procedures but does not contain a clearly labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | Yes | As an invitation to try these ideas in various experimental settings, we provide in Appendix A some Python code to generate Chebyshev learning rates and fractal schedules. |
| Open Datasets | Yes | Figure 5 shows training curves for logistic regression for MNIST classification; details are in Appendix F.3. ... Figure 6: Res Net-18/CIFAR-10 training with batch size 8192 and a repeated T = 8 fractal Chebyshev schedule. |
| Dataset Splits | No | The paper mentions using MNIST and CIFAR-10 datasets, which typically have standard splits, but it does not explicitly provide specific percentages, sample counts, or detailed splitting methodologies for training, validation, or test sets within the paper text. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware used, such as GPU or CPU models, for running its experiments. |
| Software Dependencies | No | The paper mentions 'Python code' in Appendix A but does not provide specific version numbers for Python or any other key software components, libraries, or solvers used in the experiments. |
| Experiment Setup | Yes | Figure 6: Res Net-18/CIFAR-10 training with batch size 8192 and a repeated T = 8 fractal Chebyshev schedule. |