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..
Rare Gems: Finding Lottery Tickets at Initialization
Authors: Kartik Sreenivasan, Jy-yong Sohn, Liu Yang, Matthew Grinde, Alliot Nagle, Hongyi Wang, Eric Xing, Kangwook Lee, Dimitris Papailiopoulos
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we present the experimental results for the performance of GEM-MINER across various tasks. |
| Researcher Affiliation | Collaboration | c Carnegie Mellon University m Mohamed Bin Zayed University of Artificial Intelligence p Petuum, Inc. w University of Wisconsin-Madison |
| Pseudocode | Yes | Algorithm 1: GEM-MINER |
| Open Source Code | Yes | Our codebase can be found at https://github.com/ksreenivasan/pruning_is_enough. |
| Open Datasets | Yes | We evaluate our algorithm on (Task 1) CIFAR-10 classification... (Task 2) Tiny Image Net classification... (Task 3) Finetuning on the Caltech-101 [7] dataset... and (Task 4) CIFAR-100 classification... |
| Dataset Splits | No | The paper mentions training and testing but does not explicitly provide details about a validation dataset split (e.g., percentages or sample counts for a validation set) in the main text. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., CPU/GPU models, memory specifications). |
| Software Dependencies | No | The paper mentions some algorithms and optimizers used (e.g., Adam), but does not specify software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | Yes | If a network reaches its best accuracy after E epochs of dense training, then we run GEM-MINER for E epochs from random init to get a sparse subnetwork at initialization, and then run weight training on the sparse subnetwork for another E epochs. For CIFAR-10, Mobile Net-V2 experiments, where we apply GEM-MINER for 300 epochs and then finetune the sparse model for another 300 epochs, to reach 98.6% sparse model. |