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..
On the Inherent Regularization Effects of Noise Injection During Training
Authors: Oussama Dhifallah, Yue Lu
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical results corroborate our asymptotic predictions, showing that they are accurate even in moderate problem dimensions. Our theoretical predictions are based on a new correlated Gaussian equivalence conjecture that generalizes recent results in the study of random feature models. |
| Researcher Affiliation | Academia | Oussama Dhifallah 1 Yue M. Lu 1 1O. Dhifallah and Y. M. Lu are with the John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA.. |
| Pseudocode | No | No pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | In Figure 6(a), we consider the Semeion Handwritten Digit Data Set downloaded from the Machine Learning Repository . |
| Dataset Splits | No | The paper does not explicitly provide training/test/validation dataset splits, specific percentages, or detailed splitting methodologies, although it discusses training and generalization errors. |
| Hardware Specification | No | The paper mentions numerical simulations and experiments but does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used to run them. |
| Software Dependencies | No | The paper does not provide specific software dependencies (e.g., library names with version numbers) needed to replicate the experiments. |
| Experiment Setup | Yes | Figure 1. Solid line: Theoretical predictions. Circle: Numerical simulations for (3). Black cross: Numerical simulations for (6). ϕ( ) is the sign function with probability θ of flipping the sign. bϕ( ) and σ( ) are the sign function. We set p = 500, = 0.5, α = n/p = 2, θ = 0.1, λ = 10 5. F has independent Gaussian components with zero mean and variance 1/p. |