A Mathematical Model for Curriculum Learning for Parities
Authors: Elisabetta Cornacchia, Elchanan Mossel
ICML 2023 | Conference PDF | Archive PDF | Plain Text | 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. |