What Makes Objects Similar: A Unified Multi-Metric Learning Approach
Authors: Han-Jia Ye, De-Chuan Zhan, Xue-Min Si, Yuan Jiang, Zhi-Hua Zhou
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on diverse applications exhibit the superior classification performance and comprehensibility of UM2L. Visualization results also validate its ability on physical meanings discovery. |
| Researcher Affiliation | Academia | Han-Jia Ye De-Chuan Zhan Xue-Min Si Yuan Jiang Zhi-Hua Zhou National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210023, China {yehj,zhandc,sixm,jiangy,zhouzh}@lamda.nju.edu.cn |
| Pseudocode | No | The paper presents mathematical formulations and derivations, but does not include a distinct pseudocode block or an explicitly labeled algorithm section. |
| Open Source Code | No | The paper does not provide any specific links to source code repositories or explicitly state that the code will be made publicly available. |
| Open Datasets | Yes | Social linkages come from 6 real world Facebook network datasets from [11]. |
| Dataset Splits | Yes | In each trial, 70% of instances are used for training, and the remaining part is for test. Cross-validation is employed for parameters tuning. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., CPU, GPU models, memory, or cloud instance types). |
| Software Dependencies | No | The paper mentions the use of 'FISTA [2]' (an algorithm) and 'accelerated projected gradient descent method', and 'CPLEX' (a solver) but does not provide specific version numbers for any software or libraries used. |
| Experiment Setup | Yes | Triplets are constructed with 3 targets and 10 impostors with Euclidean nearest neighbors. |