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..
Selfish Sparse RNN Training
Authors: Shiwei Liu, Decebal Constantin Mocanu, Yulong Pei, Mykola Pechenizkiy
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Using these strategies, we achieve state-of-the-art sparse training results, better than the dense-to-sparse methods, with various types of RNNs on Penn Tree Bank and Wikitext-2 datasets. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Eindhoven University of Technology, the Netherlands 2Faculty of Electrical Engineering, Mathematics, and Computer Science at University of Twente, the Netherlands |
| Pseudocode | Yes | The pseudocode of the full training procedure of our algorithm is shown in Algorithm 1. |
| Open Source Code | Yes | Our codes are available at https://github.com/ Shiweiliuiiiiiii/Selfish-RNN. |
| Open Datasets | Yes | Penn Tree Bank dataset (Marcus et al., 1993) and AWD-LSTM-Mo S on Wiki Text-2 dataset (Melis et al., 2018). |
| Dataset Splits | Yes | Single model perplexity on validation and test sets for the Penn Treebank language modeling task with stacked LSTMs and RHNs. |
| Hardware Specification | No | The paper does not provide specific hardware details (such as GPU/CPU models, memory, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions optimizers like 'Adam (Kingma & Ba, 2014)' but does not provide specific software dependency names with version numbers for libraries, frameworks, or environments used. |
| Experiment Setup | Yes | For fair comparison, we use the exact same hyperparameters and regularization introduced in ON-LSTM (Shen et al., 2019) and AWD-LSTM-Mo S (Yang et al., 2018). See Appendix A for hyperparameters. |