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..
Unmasking the Lottery Ticket Hypothesis: What's Encoded in a Winning Ticket's Mask?
Authors: Mansheej Paul, Feng Chen, Brett W. Larsen, Jonathan Frankle, Surya Ganguli, Gintare Karolina Dziugaite
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We do so through extensive empirical investigations on a range of benchmark datasets (CIFAR-10, CIFAR-100, and Image Net) and modern network architectures (Res Net-20, Res Net-18, and Res Net-50). Our contributions are as follows: |
| Researcher Affiliation | Collaboration | 1Stanford, 2Meta AI, 3Flatiron Institute, 4Mosaic ML, 5Harvard, 6Google Research, Brain Team, 7Mila; Mc Gill |
| Pseudocode | Yes | Algorithm 1: Iterative Magnitude Pruning-Weight Rewinding (IMP-WR) |
| Open Source Code | No | The paper does not state that its code is open source or provide a link to the code for the described methodology. |
| Open Datasets | Yes | We do so through extensive empirical investigations on a range of benchmark datasets (CIFAR-10, CIFAR-100, and Image Net) and modern network architectures (Res Net-20, Res Net-18, and Res Net-50). |
| Dataset Splits | No | The paper frequently refers to 'test error' but does not explicitly mention 'validation' splits or provide details for training/test/validation dataset splits. |
| Hardware Specification | No | The paper mentions 'Google Cloud research credits' and 'Meta AI' as funding/performance locations, but does not provide specific hardware details such as GPU/CPU models or specific cloud instance types. |
| Software Dependencies | No | The paper mentions 'Py Hessian package (Yao et al., 2020)' but does not provide a specific version number. Other software like 'SGD' are general algorithms without version details. |
| Experiment Setup | Yes | CIFAR-10 Res Net-20. We train with SGD and a batchsize of 128 for 62400 steps. We use lr = 0.1, momentum = 0.9, weight decay = 0.0001. The learning rate is decayed by a factor or 10 at 31200 and 46800 steps. |