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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Compressing Neural Networks using the Variational Information Bottleneck
Authors: Bin Dai, Chen Zhu, Baining Guo, David Wipf
ICML 2018 | Venue PDF | LLM Run Details | Input Tokens: 19,890 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,073 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this paper we focus on pruning individual neurons... We demonstrate state-of-the-art compression rates across an array of datasets and network architectures. |
| Researcher Affiliation | Collaboration | 1Institute for Advanced Study, Tsinghua University, Beijing, China 2Department of Computer Science, University of Maryland, USA 3Microsoft Research, Beijing, China. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks clearly labeled as such. Figure 1 shows a VIBNet Structure diagram, but it is not pseudocode. |
| Open Source Code | Yes | Code availabe at https://github.com/zhuchen03/VIBNet. |
| Open Datasets | Yes | MNIST (Le Cun, 1998)... CIFAR10 and CIFAR100 (Krizhevsky & Hinton, 2009). |
| Dataset Splits | No | The paper mentions 'test sets' but does not explicitly provide details about a validation set or how data was split for validation purposes (e.g., percentages, sample counts, or citations to predefined validation splits). |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers (e.g., TensorFlow version, PyTorch version, specific Python packages), needed to replicate the experiment. |
| Experiment Setup | Yes | During training, we set the tradeoff parameter γ using a simple heuristic such that VIBNet can roughly match the best previously reported accuracy values. In doing so we obtain a meaningful calibration of the corresponding compression results. See (Dai et al., 2018) for full details regarding this and other aspects of our model training set-up. |