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..
For self-supervised learning, Rationality implies generalization, provably
Authors: Yamini Bansal, Gal Kaplun, Boaz Barak
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also conduct an extensive empirical study of the generalization gap and the quantities used in our assumptions for a variety of self-supervision based algorithms, including Sim CLR, AMDIM and Big Bi GAN, on the CIFAR-10 and Image Net datasets. |
| Researcher Affiliation | Academia | Yamini Bansal Harvard University Gal Kaplun Harvard University Boaz Barak Harvard University |
| Pseudocode | No | The paper describes algorithms conceptually and mathematically but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | We provide our code and data in an anonymous repository on: http://github.com/ICLR2021-rep-gen/. |
| Open Datasets | Yes | We perform an empirical study of the RRM bound for a wide variety of self-supervised training methods on the Image Net (Deng et al., 2009) and CIFAR-10 (Krizhevsky et al., 2009) training datasets. |
| Dataset Splits | No | The paper frequently mentions 'train' and 'test' data but does not explicitly describe a 'validation dataset split' with specific percentages, counts, or methodology for its experiments. |
| Hardware Specification | No | The paper does not specify the hardware used for running the experiments (e.g., GPU models, CPU types, or cloud instance details). |
| Software Dependencies | No | The paper describes optimization algorithms and hyperparameters but does not list specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | Table H.3 Summary of training methods with their hyper-parameters for CIFAR-10; Table H.4 Summary of training methods with their hyper-parameters for Image Net. |