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..
One-Shot Pruning of Recurrent Neural Networks by Jacobian Spectrum Evaluation
Authors: Shunshi Zhang, Bradly C. Stadie
ICLR 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate on sequential MNIST, Billion Words, and Wikitext. Our method is evaluated with a GRU network on sequential MNIST, Wikitext, and Billion Words. |
| Researcher Affiliation | Academia | Matthew Shunshi Zhang University of Toronto EMAIL Bradly C. Stadie Vector Institute |
| Pseudocode | Yes | Algorithm 1 Pruning Recurrent Networks |
| Open Source Code | No | The paper does not contain any statement about releasing source code for the described methodology or a link to a code repository. |
| Open Datasets | Yes | We evaluate on sequential MNIST, Billion Words, and Wikitext. (Lee et al., 2018) (Chelba et al., 2013) |
| Dataset Splits | Yes | We report the training and validation perplexities on a random 1% sample of the training set in Table 4. Table 1: Validation Error % of Various 400 Unit RNN Architectures after 50 Epochs of Training on Seq. MNIST |
| Hardware Specification | Yes | We trained all networks with a single Nvidia P100 GPU. |
| Software Dependencies | No | The paper mentions the use of the Adam optimizer and Glorot initialization, but does not specify version numbers for any programming languages or software libraries used in the experiments. |
| Experiment Setup | Yes | We use a minibatch size of 64 samples during training, and optimize using the Ada M optimizer (Kingma & Ba, 2014) with a learning rate of 1e-3. We use an initial hidden state of zeros for all experiments. |