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..
Exploring Sparsity in Recurrent Neural Networks
Authors: Sharan Narang, Greg Diamos, Shubho Sengupta, Erich Elsen
ICLR 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We run all our experiments on a training set of 2100 hours of English speech data and a validation set of 3.5 hours of multi-speaker data. |
| Researcher Affiliation | Industry | Sharan Narang, Greg Diamos, Shubho Sengupta & Erich Elsen Baidu Research EMAIL Now at Google Brain EMAIL |
| Pseudocode | Yes | Algorithm 1 Pruning Algorithm |
| Open Source Code | No | The paper does not provide an unambiguous statement or link for the open-source code of their methodology. |
| Open Datasets | No | The paper states, "We run all our experiments on a training set of 2100 hours of English speech data and a validation set of 3.5 hours of multi-speaker data. This is a small subset of the datasets that we use to train our state-of-the-art automatic speech recognition models.", but does not provide any information about public availability or access. |
| Dataset Splits | Yes | We run all our experiments on a training set of 2100 hours of English speech data and a validation set of 3.5 hours of multi-speaker data. |
| Hardware Specification | Yes | The performance benchmark was run using NVIDIA s CUDNN and cu SPARSE libraries on a Titan X Maxwell GPU and compiled using CUDA 7.5. |
| Software Dependencies | Yes | The performance benchmark was run using NVIDIA s CUDNN and cu SPARSE libraries on a Titan X Maxwell GPU and compiled using CUDA 7.5. |
| Experiment Setup | Yes | We train the models using Nesterov SGD for 20 epochs. Besides the hyper-parameters for determining the threshold, all other hyper-parameters remain unchanged between the dense and sparse training runs. In the sparse run, the pruning begins shortly after the first epoch and continues until the 10th epoch. |