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..
On Warm-Starting Neural Network Training
Authors: Jordan Ash, Ryan P. Adams
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct a series of experiments across several different architectures, optimizers, and image datasets. |
| Researcher Affiliation | Collaboration | Jordan T. Ash Microsoft Research NYC EMAIL, Ryan P. Adams Princeton University EMAIL |
| Pseudocode | No | No pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code for the methodology described or a link to a code repository. |
| Open Datasets | Yes | Models are ο¬tted to the CIFAR-10, CIFAR-100, and SVHN image data. All models are trained using a mini-batch size of 128 and a learning rate of 0.001. |
| Dataset Splits | Yes | Presented results are on a held-out, randomly-chosen third of available data. ... validation sets composed of a random third of available data... |
| Hardware Specification | Yes | Wall-clock time is measured by assigning every model identical resources, consisting of 50GB of RAM and an NVIDIA Tesla P100 GPU. |
| Software Dependencies | No | The paper mentions optimizers (SGD, Adam [17]) but does not provide specific version numbers for any software libraries or frameworks (e.g., Python, PyTorch, TensorFlow). |
| Experiment Setup | Yes | All models are trained using a mini-batch size of 128 and a learning rate of 0.001... We explore all combinations of batch sizes {16, 32, 64, 128}, and learning rates {0.001, 0.01, 0.1}... |