Simple and Effective Stochastic Neural Networks
Authors: Tianyuan Yu, Yongxin Yang, Da Li, Timothy Hospedales, Tao Xiang3252-3260
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | 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. |