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..
Calibrating the Rigged Lottery: Making All Tickets Reliable
Authors: Bowen Lei, Ruqi Zhang, Dongkuan Xu, Bani Mallick
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on multiple datasets, model architectures, and sparsities show that our method reduces ECE values by up to 47.8% and simultaneously maintains or even improves accuracy with only a slight increase in computation and storage burden. |
| Researcher Affiliation | Academia | Bowen Lei Texas A&M University EMAIL Dongkuan Xu North Carolina State University EMAIL Ruqi Zhang Purdue University EMAIL Bani Mallick Texas A&M University EMAIL |
| Pseudocode | Yes | Algorithm 1 Cig L |
| Open Source Code | Yes | The implementation code can be found in https://github.com/Steven Boys/Cig L. |
| Open Datasets | Yes | Our experiments are based on three benchmark datasets: CIFAR-10 and CIFAR100 (Krizhevsky et al., 2009) and Image Net-2012 (Russakovsky et al., 2015). |
| Dataset Splits | No | The paper mentions training models and using a validation set for temperature scaling but does not specify the train/validation/test dataset splits (e.g., percentages or sample counts) for its experiments. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions "Py Torch" as the code platform but does not specify a version number or other software dependencies with their versions. |
| Experiment Setup | Yes | The parameters are optimized by SGD with momentum. For the learning rate, we use piecewise constant decay scheduler. For CIFAR-10 and CIFAR-100, we train all the models for 250 epochs with a batch size of 128. For Image Net, we train all the models for 100 epochs with a batch size of 64. |