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..
Reducing Network Agnostophobia
Authors: Akshay Raj Dhamija, Manuel Günther, Terrance Boult
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on networks trained to classify classes from MNIST and CIFAR-10 show that our novel loss functions are significantly better at dealing with unknown inputs from datasets such as Devanagari, Not MNIST, CIFAR-100, and SVHN. |
| Researcher Affiliation | Academia | Akshay Raj Dhamija, Manuel G unther, and Terrance E. Boult Vision and Security Technology Lab, University of Colorado Colorado Springs {adhamija | mgunther | tboult} @ vast.uccs.edu |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is publicly available.1 1http://github.com/Vastlab/Reducing-Network-Agnostophobia |
| Open Datasets | Yes | Experiments on networks trained to classify classes from MNIST and CIFAR-10 show that our novel loss functions are significantly better at dealing with unknown inputs from datasets such as Devanagari, Not MNIST, CIFAR-100, and SVHN. |
| Dataset Splits | No | The paper does not provide specific train/validation/test dataset splits with percentages or sample counts for reproduction, nor does it explicitly mention the use of a dedicated validation set for model tuning. |
| Hardware Specification | No | The paper does not specify the hardware used to run experiments, such as specific GPU or CPU models, or details about the computing environment. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python 3.x, TensorFlow x.x, PyTorch x.x) for reproducing the experiments. |
| Experiment Setup | No | The paper does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs) or detailed training configurations in the main text. |