Approximate Conditional Gradient Descent on Multi-Class Classification
Authors: Zhuanghua Liu, Ivor Tsang
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results verify that our method outperforms the state-of-the-art stochastic projectionfree methods. |
| Researcher Affiliation | Academia | Zhuanghua Liu, Ivor Tsang Centre for Artifical Intelligence University of Technology Sydney liuzhuanghua1991@gmail.com Ivor.Tsang@uts.edu.au |
| Pseudocode | Yes | Algorithm 1 Approximate Frank-Wolfe |
| Open Source Code | No | The paper does not provide any explicit statement or link for open-source code for the methodology described. |
| Open Datasets | Yes | We conducted our experiment on several large-scale datasets from the libsvm website1. The datasets are summarized in the following table: [...] 1https://www.csie.ntu.edu.tw/ cjlin/libsvmtools/datasets/ |
| Dataset Splits | No | The paper mentions using "training data" and "datasets" for evaluation but does not specify clear train/validation/test splits, percentages, or absolute sample counts for data partitioning. No mention of a "validation" set. |
| Hardware Specification | Yes | Our experiments run on a server with 3.1 GHZ CPU and 132 GB memory. |
| Software Dependencies | No | Our algorithm and baseline methods are implemented in Matlab. (No version number for Matlab is provided.) |
| Experiment Setup | Yes | In the evaluation of approximate Frank Wolfe, we set ν = 50, δ = 10 and k0 = 100. For fair comparison, we use the default parameter for SFW and SVRF from (Hazan and Luo 2016), i.e., The size of mini-batches at round t is t2, t for SFW and SVRF, respectively. |