Signing the Supermask: Keep, Hide, Invert
Authors: Nils Koster, Oliver Grothe, Achim Rettinger
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The following paragraphs present the experiments conducted in order to assess the validity and effectiveness of fixed threshold signed Supermasks. The code for the experiments can be found here. |
| Researcher Affiliation | Academia | Nils Koster Department of Applied Econometrics Karlsruhe Institute of Technology nils.koster@kit.edu Oliver Grothe Department of Analytics and Statistics Karlsruhe Institute of Technology oliver.grothe@kit.edu Achim Rettinger Department of Computational Linguistics and Digital Humanities Trier University rettinger@uni-trier.de |
| Pseudocode | No | The paper includes mathematical formulations and descriptions of processes but does not present any explicitly labeled pseudocode blocks or algorithms. |
| Open Source Code | Yes | The code is available here. |
| Open Datasets | Yes | The fully-connected (FCN) model is trained on MNIST (Le Cun et al., 2010) and all CNNs plus Res Net20 on CIFAR-10 (Krizhevsky, 2009). Res Net56 and Res Net110 are trained on CIFAR-100 (Krizhevsky, 2009)... All datasets used in this work are provided by tensorflow here. |
| Dataset Splits | No | The paper specifies datasets used for training and testing, and describes preprocessing steps like image standardization and augmentation (for CIFAR-100), but it does not provide explicit details about validation set splits (e.g., percentages or sample counts for a validation set). |
| Hardware Specification | No | The paper states that 'all experiments were run on a single GPU' but does not specify the model or type of GPU, CPU, or any other detailed hardware specifications. |
| Software Dependencies | No | The paper mentions that 'The code is written in Python, mainly using tensorflow and numpy.' However, it does not provide specific version numbers for Python, TensorFlow, NumPy, or any other software libraries. |
| Experiment Setup | Yes | Tables 8, 9, 10, and 11 in Appendix B summarize hyperparameter choices for training both baseline and signed Supermask models, including learning rates, decay rates, weight decay, momentum, and iterations. |