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..
Learning sparse features can lead to overfitting in neural networks
Authors: Leonardo Petrini, Francesco Cagnetta, Eric Vanden-Eijnden, Matthieu Wyart
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate empirically that learning sparse features can lead to severe overfitting in neural networks. Our empirical results suggest that the overfitting phenomenon is caused by the model’s over-reliance on a few sparse features... For our experiments, we perform extensive experiments on CIFAR-10, CIFAR-100 and ImageNet datasets. |
| Researcher Affiliation | Academia | Hao Li, Guanxiong Liu, Sheng Li, Binghui Wang, Carnegie Mellon University. Yu-Gang Jiang, Fudan University. |
| Pseudocode | No | The paper describes methods and strategies in natural language and mathematical equations, but it does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | Yes | For our experiments, we perform extensive experiments on CIFAR-10, CIFAR-100 and ImageNet datasets. ... For ImageNet dataset, we preprocess ImageNet following [14]. |
| Dataset Splits | Yes | We use the standard 50k/10k train/test split for CIFAR-10 and CIFAR-100. For ImageNet dataset, we preprocess ImageNet following [14]. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments, such as exact GPU or CPU models. It only implies usage of GPUs without specifications. |
| Software Dependencies | No | The paper mentions software like PyTorch and the SGD optimizer (Appendix A), but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | We use SGD optimizer with momentum 0.9 and weight decay 5e-4. For CIFAR-10 and CIFAR-100, the models are trained for 200 epochs with a batch size of 128. The learning rate is initialized to 0.1 and divided by 10 at epochs 100 and 150. For ImageNet, the models are trained for 100 epochs with a batch size of 256. The learning rate is initialized to 0.1 and divided by 10 at epochs 30, 60 and 90. |