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..
A Mathematical Model for Curriculum Learning for Parities
Authors: Elisabetta Cornacchia, Elchanan Mossel
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In all our experiments we use fully connected ReLU networks and we train them by SGD on the square loss. |
| Researcher Affiliation | Academia | 1Institute of Mathematics, EPFL, Lausanne, Switzerland 2Department of Mathematics and IDSS, MIT, US. |
| Pseudocode | No | The paper describes mathematical update rules and algorithmic steps within the text, but does not include a formal 'Algorithm' or 'Pseudocode' block. |
| Open Source Code | Yes | Code: https://github.com/ecornacchia/Curriculum-Learning-for-Parities |
| Open Datasets | No | The paper describes generating samples from specific distributions (e.g., Rad(p)d) rather than using a pre-existing, publicly available dataset with concrete access information or citations. |
| Dataset Splits | No | The paper describes training strategies and implicit testing but does not provide explicit training, validation, or test dataset split percentages or sample counts. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions using ReLU networks and training with SGD on square loss, but does not specify versions for software libraries or dependencies within the text. |
| Experiment Setup | Yes | In all plots, we use a 2-layers Re LU MLP with batch size 1024, input dimension 100, and 100 hidden units. We run a 2-steps curriculum strategy for 3 values of p1, namely p1 = 39/40, 19/20, 1/20. |