Net-Trim: Convex Pruning of Deep Neural Networks with Performance Guarantee
Authors: Alireza Aghasi, Afshin Abdi, Nam Nguyen, Justin Romberg
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we present some experiments to highlight its performance against the state of the art techniques. We next apply Net-Trim to the problem of classifying hand-written digits of the mixed national institute of standards and technology (MNIST) dataset. |
| Researcher Affiliation | Collaboration | Alireza Aghasi Institute for Insight Georgia State University IBM TJ Watson aaghasi@gsu.edu Afshin Abdi Department of ECE Georgia Tech abdi@gatech.edu Nam Nguyen IBM TJ Watson nnguyen@us.ibm.com Justin Romberg Department of ECE Georgia Tech jrom@ece.gatech.edu |
| Pseudocode | Yes | Algorithm 1 Parallel Net-Trim |
| Open Source Code | Yes | The authors have made the implementation publicly available online3. 3The code for the regularized Net-Trim implementation using the ADMM scheme can be accessed online at: https://github.com/DNNTool Box/Net-Trim-v1 |
| Open Datasets | Yes | We next apply Net-Trim to the problem of classifying hand-written digits of the mixed national institute of standards and technology (MNIST) dataset. The set contains 60,000 training samples and 10,000 test instances. |
| Dataset Splits | No | The paper states 'The set contains 60,000 training samples and 10,000 test instances' for the MNIST dataset, but does not explicitly mention a validation split. |
| Hardware Specification | No | The paper mentions distributing jobs 'among a cluster of processing units (in our case 64) or using a GPU', but does not provide specific details such as GPU model numbers or CPU types. |
| Software Dependencies | No | The paper states 'The authors have made the implementation publicly available online3. 3The code for the regularized Net-Trim implementation using the ADMM scheme can be accessed online at: https://github.com/DNNTool Box/Net-Trim-v1' and mentions using ADMM, but it does not specify version numbers for any software dependencies or solvers used in the experiments. |
| Experiment Setup | No | The paper describes network architectures (e.g., '784 300 300 10 network', 'two convolutional layers composed of 32 filters of size 5 5 1'), and the number of training samples, but it does not provide specific hyperparameter values such as learning rates, batch sizes, or number of epochs for training the neural networks. |