Asynchronous Doubly Stochastic Sparse Kernel Learning
Authors: Bin Gu, Miao Xin, Zhouyuan Huo, Heng Huang
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Importantly, the experimental results on various large-scale real-world datasets show that, our Asy DSSKL method has the significant superiority on the computational efficiency at the training and predicting steps over the existing kernel methods. |
| Researcher Affiliation | Academia | Bin Gu,1 Xin Miao,2 Zhouyuan Huo,1 Heng Huang1* 1Department of Electrical & Computer Engineering, University of Pittsburgh, USA 2Dept. of Computer Science and Engineering, University of Texas at Arlington, USA big10@pitt.edu, xin.miao@mavs.uta.edu, zhouyuan.huo@pitt.edu, heng.huang@pitt.edu |
| Pseudocode | Yes | Algorithm 1 Asynchronous sparse random feature learning framework, Algorithm 2 Asynchronous doubly stochastic sparse kernel learning algorithm (Asy DSSKL) |
| Open Source Code | No | The paper states 'We implement our Asy DSSKL in C++', but does not provide any specific link or explicit statement about releasing the source code for Asy DSSKL. |
| Open Datasets | Yes | Datasets: Table 3 summarizes the six large-scale real-world datasets used in our experiments. They are the Covtype B, RCV1, SUSY, Covtype M, MNIST and Aloi datasets which are from https://www.csie.ntu.edu.tw/ cjlin/ libsvmtools/datasets/. |
| Dataset Splits | No | The paper mentions 'training set' and 'testing set' but does not specify explicit train/validation/test dataset splits by percentage, absolute sample counts, or refer to predefined standard splits. |
| Hardware Specification | Yes | Our experiments are performed on a 32-core two-socket Intel Xeon E5-2699 machine where each socket has 16 cores. |
| Software Dependencies | No | The paper mentions 'C++' and 'Open MP' for implementation but does not specify their version numbers or any other software dependencies with version numbers. |
| Experiment Setup | Yes | In the experiments, the value of steplength γ is selected from {102; 10; 1; 10 1; 10 2; 10 3; 10 4; 10 5}. The # of inner loop iterations m is set as the size of training set, and the # of outer loop iterations S is set as 10. |