Compressing Neural Networks using the Variational Information Bottleneck
Authors: Bin Dai, Chen Zhu, Baining Guo, David Wipf
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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. |