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 both Weights and Connections for Efficient Neural Network
Authors: Song Han, Jeff Pool, John Tran, William Dally
NeurIPS 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We implemented network pruning in Caffe [26]. Caffe was modified to add a mask which disregards pruned parameters during network operation for each weight tensor. The pruning threshold is chosen as a quality parameter multiplied by the standard deviation of a layer s weights. We carried out the experiments on Nvidia Titan X and GTX980 GPUs. We pruned four representative networks: Lenet-300-100 and Lenet-5 on MNIST, together with Alex Net and VGG-16 on Image Net. The network parameters and accuracy 1 before and after pruning are shown in Table 1. |
| Researcher Affiliation | Collaboration | Song Han Stanford University EMAIL Jeff Pool NVIDIA EMAIL John Tran NVIDIA EMAIL William J. Dally Stanford University NVIDIA EMAIL |
| Pseudocode | No | The paper contains 'Figure 2: Three-Step Training Pipeline' which illustrates the process, but no structured pseudocode or algorithm blocks are provided. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing open-source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | On the Image Net dataset, our method reduced the number of parameters of Alex Net by a factor of 9 , from 61 million to 6.7 million, without incurring accuracy loss. Similar experiments with VGG-16 found that the total number of parameters can be reduced by 13 , from 138 million to 10.3 million, again with no loss of accuracy. We first experimented on MNIST dataset with the Le Net-300-100 and Le Net-5 networks [4]. |
| Dataset Splits | Yes | We further examine the performance of pruning on the Image Net ILSVRC-2012 dataset, which has 1.2M training examples and 50k validation examples. |
| Hardware Specification | Yes | We carried out the experiments on Nvidia Titan X and GTX980 GPUs. |
| Software Dependencies | No | The paper states, 'We implemented network pruning in Caffe [26],' but it does not provide a specific version number for Caffe or any other software dependencies. |
| Experiment Setup | Yes | After pruning, the network is retrained with 1/10 of the original network s original learning rate. (LeNet) After pruning, the whole network is retrained with 1/100 of the original network s initial learning rate. (AlexNet) |