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..
Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning
Authors: Mehdi Sajjadi, Mehran Javanmardi, Tolga Tasdizen
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the proposed method on several benchmark datasets. |
| Researcher Affiliation | Academia | Department of Electrical and Computer Engineering University of Utah EMAIL |
| Pseudocode | No | The paper describes the proposed method and loss functions but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions using existing frameworks (cuda-convnet [37] and sparse convolutional networks [38, 39]) but does not state that the code for the proposed methodology is openly provided. |
| Open Datasets | Yes | We show the effect of the proposed unsupervised loss functions using Conv Nets on MNIST [2], CIFAR10 and CIFAR100 [34], SVHN [35], NORB [36] and ILSVRC 2012 challenge [5]. |
| Dataset Splits | Yes | We randomly select 10 samples from each class (total of 100 labeled samples). We use all available training data as the unlabeled set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'cuda-convnet' and 'sparse convolutional networks' frameworks but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | In Eq. 1, we set n to be 4 for experiments conducted using cuda-convnet and 5 for experiments performed using sparse convolutional networks. |