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..
Perturbation Analysis of Neural Collapse
Authors: Tom Tirer, Haoxiang Huang, Jonathan Niles-Weed
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We support our theory with experiments in practical deep learning settings. |
| Researcher Affiliation | Academia | 1Faculty of Engineering, Bar-Ilan University, Ramat Gan, Israel 2Courant Institute of Mathematical Sciences, New York University, NY, US. |
| Pseudocode | No | The paper does not contain any sections or figures explicitly labeled 'Pseudocode' or 'Algorithm'. |
| Open Source Code | No | The paper does not provide any statements about releasing code for the methodology or links to a code repository. |
| Open Datasets | Yes | We consider the CIFAR-10 dataset and train an MLP... We consider the CIFAR-10 dataset and examine how modifying the regularization hyperparameters affects the NC behavior of the widely used Res Net18 (He et al., 2016a)... In Figure 4 we consider the MNIST dataset with 3K training samples per class. |
| Dataset Splits | No | The paper mentions using 'training samples per class' for CIFAR-10 and MNIST, but it does not explicitly state the dataset splits (e.g., specific percentages for training, validation, and testing sets, or refer to standard predefined splits). |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions 'default Py Torch initialization' but does not specify version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | Specifically, as a baseline hyperparameter setting, we consider one that is used in previous works (Papyan et al., 2020; Zhu et al., 2021): default Py Torch initialization of the weights, SGD optimizer with LR 0.05 that is divided by 10 every 40 epochs, momentum of 0.9, and WD of 5e-4 for all the network s parameters. |