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..
Semi-Supervised Learning with Variational Bayesian Inference and Maximum Uncertainty Regularization
Authors: Kien Do, Truyen Tran, Svetha Venkatesh7236-7244
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show clear improvements in classification errors of various CR based methods when they are combined with VBI or MUR or both. |
| Researcher Affiliation | Academia | Applied Artificial Intelligence Institute (A2I2), Deakin University, Geelong, Australia EMAIL |
| Pseudocode | No | No explicit pseudocode or algorithm blocks are provided. The methodology is described using mathematical equations and textual explanations. |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | We evaluate our approaches on three standard benchmark datasets: SVHN, CIFAR-10 and CIFAR-100. |
| Dataset Splits | No | The paper mentions 'labeled and unlabeled training datasets' (Dl, Du) and uses different numbers of labeled samples (e.g., 500, 1000, 250 on SVHN; 1000, 2000, 4000, 10000 on CIFAR-10/100). However, it does not explicitly provide specific train/validation/test split percentages or absolute counts for the datasets in the main text, nor does it refer to standard splits with citations that would provide this detail. |
| Hardware Specification | No | The paper does not explicitly provide specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments in the main text. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library names like PyTorch, TensorFlow, or specific Python versions) used in the experiments. |
| Experiment Setup | Yes | The paper discusses specific experimental setup details such as the coefficient of DKL (qφ(w) p(w)) in VBI, the radius r in MUR, and learning rates (α) and number of steps (s) for iterative approximations of x. For example, 'Fig. 2c shows the error of MT+MUR on CIFAR-10 with 1000 labels as a function of r (r {4, 7, 10, 20, 40}).' and 'We try both projected gradient ascent (PGA) and vanilla gradient ascent (GA) updates with the learning rate α varying in {0.1, 1.0, 10.0} and the number of steps s varying in {2, 5, 8}.' |