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..
Robust Pruning at Initialization
Authors: Soufiane Hayou, Jean-Francois Ton, Arnaud Doucet, Yee Whye Teh
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This allows us to propose novel principled approaches which we validate experimentally on a variety of NN architectures. |
| Researcher Affiliation | Academia | Sou๏ฌane Hayou, Jean-Francois Ton, Arnaud Doucet & Yee Whye Teh Department of Statistics University of Oxford United Kingdom EMAIL |
| Pseudocode | Yes | Algorithm 1 Rescaling trick for FFNN |
| Open Source Code | No | The paper does not include any explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | We validate the results on MNIST, CIFAR10, CIFAR100 and Tiny Image Net. |
| Dataset Splits | Yes | We validate the results on MNIST, CIFAR10, CIFAR100 and Tiny Image Net. |
| Hardware Specification | No | The paper does not specify any hardware used for running the experiments. |
| Software Dependencies | No | The paper mentions 'SGD' as an optimizer and activation functions like 'tanh', 'ReLU', 'ELU', but does not list any specific software libraries, frameworks, or their version numbers. |
| Experiment Setup | Yes | We use SGD with batchsize 100 and learning rate 10 3, which we found to be optimal using a grid search with an exponential scale of 10. |