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..
The Benefits of Mixup for Feature Learning
Authors: Difan Zou, Yuan Cao, Yuanzhi Li, Quanquan Gu
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results verify our theoretical ๏ฌndings and demonstrate the effectiveness of the early-stopped Mixup training. |
| Researcher Affiliation | Academia | 1Department of Computer Science & Institute of Data Science, The University of Hong Kong 2Department of Statistics and Actuarial Science & Department of Mathematics, The University of Hong Kong 3Machine Learning Department, Carnegie Mellon University 4Department of Computer Science, University of California, Los Angeles. |
| Pseudocode | No | The paper describes training algorithms using mathematical equations and textual descriptions, but it does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code for the described methodology, nor does it include any links to a code repository. |
| Open Datasets | Yes | We conduct a proof-of-concept experiment on CIFAR-10 dataset. |
| Dataset Splits | No | The paper mentions using CIFAR-10 and synthetic data but does not specify any training/validation/test dataset splits (e.g., percentages, sample counts, or references to predefined splits). |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, or cloud resources) used to run the experiments. |
| Software Dependencies | No | The paper mentions using 'SGD with momentum 0.9' and 'Res Net18 model', but it does not specify version numbers for any software components, libraries, or frameworks used in the experiments. |
| Experiment Setup | Yes | For the two-layer CNN model and the training algorithm, we set network width m = 10, and conduct full-batch gradient descent with learning rate ฮท = 0.05 and total iteration number T = 20000. ... For Res Net18 and Res Net34, we set the learning rate as 0.1; for Le Net and VGG16, we set the learning rate as 0.02 and 0.1 respectively. |