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..
The Elastic Lottery Ticket Hypothesis
Authors: Xiaohan Chen, Yu Cheng, Shuohang Wang, Zhe Gan, Jingjing Liu, Zhangyang Wang
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on CIFAR-10 and Image Net, and propose a variety of strategies to tweak the winning tickets found from different networks of the same model family (e.g., Res Nets). |
| Researcher Affiliation | Collaboration | Xiaohan Chen1 Yu Cheng2 Shuohang Wang2 Zhe Gan2 Jingjing Liu3 Zhangyang Wang1 1University of Texas at Austin 2Microsoft Corporation 3Tsinghua University |
| Pseudocode | No | No structured pseudocode or algorithm blocks found. The IMP algorithm steps are described in paragraph form. |
| Open Source Code | Yes | Code is available at https://github.com/VITA-Group/Elastic LTH. |
| Open Datasets | Yes | We conduct extensive experiments on CIFAR-10 [26] and then Image Net [7] |
| Dataset Splits | Yes | We conduct extensive experiments on CIFAR-10 [26] and then Image Net [7], transferring the winning tickets across multiple models from Res Net family and VGG family. |
| Hardware Specification | No | No specific hardware details (e.g., exact GPU/CPU models, memory amounts) are mentioned for running experiments. |
| Software Dependencies | No | No specific ancillary software details (e.g., library or solver names with version numbers) are mentioned in the provided text. |
| Experiment Setup | No | No specific experimental setup details (e.g., concrete hyperparameter values, training configurations, or system-level settings) are explicitly provided in the main text of the paper. |