Calibrating the Rigged Lottery: Making All Tickets Reliable
Authors: Bowen Lei, Ruqi Zhang, Dongkuan Xu, Bani Mallick
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | 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 bowenlei@stat.tamu.edu Dongkuan Xu North Carolina State University dxu27@ncsu.edu Ruqi Zhang Purdue University ruqiz@purdue.edu Bani Mallick Texas A&M University bmallick@stat.tamu.edu |
| 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. |