Two-Sided Wasserstein Procrustes Analysis
Authors: Kun Jin, Chaoyue Liu, Cathy Xia
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experiment TWP over three applications: protein alignment, language alignment and domain adaptation and demonstrate competitive performance of TWP compared with state-of-the-art methods. |
| Researcher Affiliation | Academia | Kun Jin1 , Chaoyue Liu1 , Cathy Xia2 1Department of Computer Science and Engineering, The Ohio State University 2Department of Integrated Systems Engineering, The Ohio State University |
| Pseudocode | Yes | Algorithm 1 TWP |
| Open Source Code | No | The paper does not provide any specific repository link or explicit statement about the release of source code for the described methodology. |
| Open Datasets | Yes | We use the data from [Wang and Mahadevan, 2008], where Glutaredoxin protein PDB-1G7O composed of 215 amino acids is used. |
| Dataset Splits | No | The paper discusses the construction of datasets and their use in experiments, but it does not provide specific training/validation/test split percentages or sample counts for reproducibility. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, processor types, or memory amounts used for running the experiments. |
| Software Dependencies | No | The paper mentions algorithms like Sinkhorn Knoop and Gauss-Seidel method but does not provide specific software names with version numbers for reproducibility, such as Python library versions or specialized solvers. |
| Experiment Setup | Yes | To apply TWP, we set the dimension of the common embedding space to be d = 3 (3D), d = 2 (2D) and d = 1 (1D), respectively. |