Learning Distance for Sequences by Learning a Ground Metric
Authors: Bing Su, Ying Wu
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on several sequence datasets demonstrate the effectiveness and efficiency of our method. |
| Researcher Affiliation | Academia | 1Science & Technology on Integrated Information System Laboratory, Institute of Software, Chinese Academy of Sciences, Beijing, China 2Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, USA. |
| Pseudocode | Yes | Algorithm 1 RVSML |
| Open Source Code | No | The paper does not provide a specific link or explicit statement about the public availability of its source code. |
| Open Datasets | Yes | MSR Action3D dataset (Li et al., 2010) contains 567 depth video sequences from 20 action classes. |
| Dataset Splits | Yes | We follow the splits in (Wang et al., 2012; Wang & Wu, 2013) to divide the dataset into training and testing sets. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, or memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch versions) needed to replicate the experiment. |
| Experiment Setup | Yes | The parameters λ1, λ2, and σ of OPW are fixed to 10, 0.1 and 12, respectively, on the MSR Activity3D dataset, and 50, 0.1 and 1, respectively, on other datasets, as suggested in (Su & Hua, 2018)... For RVSML, we select m in the range of 2 to 8 with an interval of 2 and set β to a small value via cross-validation. |