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..
Simple and Effective Stochastic Neural Networks
Authors: Tianyuan Yu, Yongxin Yang, Da Li, Timothy Hospedales, Tao Xiang3252-3260
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments are carried out to evaluate the efficacy of the proposed framework in four applications: neural network pruning, adversarial attack defense, learning with label noise, and model calibration. |
| Researcher Affiliation | Collaboration | Tianyuan Yu1, Yongxin Yang1, Da Li2,3, Timothy Hospedales2,3, Tao Xiang1 1Center for Vision, Speech and Signal Processing, University of Surrey 2School of Informatics, University of Edinburgh 3Samsung AI Centre, Cambridge |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an unambiguous statement or direct link to the source code for the methodology described in the paper. It mentions “1https://github.com/BVLC/caffe/tree/master/examples/mnist”, but this is a link to a third-party framework’s example, not the authors’ own code for their method. |
| Open Datasets | Yes | We follow the architecture/dataset combinations used in most recent neural network pruning studies, including Le Net-5-Caffe1 network on MNIST (Le Cun et al. 1998), VGG-16 (Simonyan and Zisserman 2015) on CIFAR10 (Krizhevsky and Hinton 2009) and a variant of VGG 16 on CIFAR100. |
| Dataset Splits | Yes | T is optimized with respect to validation negative log likelihood. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or cloud instance types) used for running its experiments. |
| Software Dependencies | No | The paper mentions “Le Net-5-Caffe” but does not specify version numbers for Caffe or any other software dependencies used in the experiments. |
| Experiment Setup | Yes | We set the regularizer weight ω (Eq. 9) and margin b (Eq. 5) as 0.01 and 4 in all experiments, respectively. |