Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Structured Bayesian Pruning via Log-Normal Multiplicative Noise
Authors: Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha, Dmitry P. Vetrov
NeurIPS 2017 | Venue PDF | LLM Run Details | Input Tokens: 16,271 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,357 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 | Our experiments show that our model leads to high group sparsity level and significant acceleration of convolutional neural networks with negligible accuracy drop. We demonstrate the performance of our method on Le Net and VGG-like architectures using MNIST and CIFAR-10 datasets. |
| Researcher Affiliation | Collaboration | 1National Research University Higher School of Economics 2Yandex 3Skolkovo Institute of Science and Technology |
| Pseudocode | No | The paper describes methods and procedures in detail but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code is available in Theano [7] and Lasagne, and also in Tensor Flow [1] (https://github.com/necludov/group-sparsity-sbp). |
| Open Datasets | Yes | We demonstrate the performance of our method on Le Net and VGG-like architectures using MNIST and CIFAR-10 datasets. |
| Dataset Splits | No | The paper mentions using MNIST and CIFAR-10 datasets for training and evaluation, but it does not specify explicit train/validation/test dataset splits (e.g., percentages, sample counts, or citations to predefined splits). |
| Hardware Specification | Yes | We report acceleration that was computed on CPU (Intel Xeon E5-2630), GPU (Tesla K40) |
| Software Dependencies | No | The paper mentions using 'Theano' and 'Tensor Flow' and provides general references, but it does not specify exact version numbers for these or any other software dependencies needed for reproducibility. |
| Experiment Setup | Yes | The truncation parameters a and b are the hyperparameters of our model... We use values a = 20 and b = 0 in all experiments. |