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..
A Constructive Prediction of the Generalization Error Across Scales
Authors: Jonathan S. Rosenfeld, Amir Rosenfeld, Yonatan Belinkov, Nir Shavit
ICLR 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically explore the behavior of the generalization error over a wide range of datasets and models in vision and language tasks. |
| Researcher Affiliation | Collaboration | Jonathan S. Rosenfeld1 Amir Rosenfeld2 Yonatan Belinkov13 Nir Shavit145 EMAIL EMAIL 1 Massachusetts Institute of Technology 2 York University 3 Harvard University 4 Neural Magic Inc 5 Tel Aviv University |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain an explicit statement about open-sourcing code or a link to a code repository. |
| Open Datasets | Yes | Image Net (Russakovsky et al., 2015): a large-scale recognition benchmark... CIFAR10/100 (Krizhevsky et al., 2009)... DTD (Cimpoi et al., 2014)... Aircraft (Maji et al., 2013)... UCF101 (Soomro et al., 2012)... Penn Treebank (Mikolov et al., 2010)... Wiki Text-2 (Bradbury et al., 2017)... Wiki Text-103 (Merity et al., 2016). |
| Dataset Splits | Yes | CIFAR10/100 (Krizhevsky et al., 2009): 60K natural RGB images of 10 classes (100 for CIFAR100) with a train/test split of 50K/10K. ... PTB... 900K/70K/80K training/validation/test words. |
| Hardware Specification | No | The paper does not specify any particular hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions software like PyTorch, SGD, and Adam but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | Hyper-parameters: For similar reasons we wish to avoid hyper-paramater search at large scales, and thus avoid the temptation to tune hyper-parameters accordingly (learning rate, regularization, etc.). Therefore, we hold all hyper-parameters fixed. ... In the main experiments, training is done via SGD with a momentum of 0.9, weight decay of 1e-4 and initial learning rate of 0.1. For Image Net we train for 90 epochs, decreasing the learning rate by a multiplicative factor of 0.1 after and 30 and after 60 epochs. We use a batch size of 16. For all other vision datasets we use a batch-size of 128. We begin training with a learning rate of 0.1, run for 200 epochs, and reduce by a multiplicative factor of 0.1 after 80, 120, and 160 epochs. |