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..
Learning Structured Sparsity in Deep Neural Networks
Authors: Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, Hai Li
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiments We evaluate the effectiveness of our SSL using published models on three databases MNIST, CIFAR-10, and Image Net. Without explicit explanation, SSL starts with the network whose weights are initialized by the baseline, and speedups are measured in matrix-matrix multiplication by Caffe in a single-thread Intel Xeon E5-2630 CPU. |
| Researcher Affiliation | Academia | Wei Wen University of Pittsburgh EMAIL Chunpeng Wu University of Pittsburgh EMAIL Yandan Wang University of Pittsburgh EMAIL Yiran Chen University of Pittsburgh EMAIL Hai Li University of Pittsburgh EMAIL |
| Pseudocode | No | The paper describes its method through mathematical formulations and textual explanations but does not include any pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our source code can be found at https://github.com/wenwei202/caffe/tree/scnn. |
| Open Datasets | Yes | We evaluate the effectiveness of our SSL using published models on three databases MNIST, CIFAR-10, and Image Net. |
| Dataset Splits | Yes | A 227 227 image is randomly cropped from each scaled image and mirrored for data augmentation and only the center crop is used for validation. |
| Hardware Specification | Yes | speedups are measured in matrix-matrix multiplication by Caffe in a single-thread Intel Xeon E5-2630 CPU. and on CPU (Intel Xeon) and GPU (Ge Force GTX TITAN Black). Figure 7(c) shows speedups of ℓ1-norm and SSL on various platforms, including both GPU (Quadro, Tesla and Titan) and CPU (Intel Xeon E5-2630). |
| Software Dependencies | No | The paper mentions software like 'Caffe', 'Intel Math Kernel Library', and 'CUDA cu BLAS and cu SPARSE', but it does not specify any version numbers for these software dependencies. |
| Experiment Setup | Yes | Hyper-parameters are selected by cross-validation. we added a dropout layer with a ratio of 0.5 in the fully-connected layer to avoid over-fitting. (for Conv Net). For ResNet, it states the net is finally fine-tuned with a base learning rate of 0.01, which is lower than that (i.e., 0.1) in the baseline. |