Geometric All-way Boolean Tensor Decomposition
Authors: Changlin Wan, Wennan Chang, Tong Zhao, Sha Cao, Chi Zhang
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on both synthetic and real-world data demonstrated that GETF has significantly improved performance in reconstruction accuracy, extraction of latent structures and it is an order of magnitude faster than other state-of-the-art methods. |
| Researcher Affiliation | Collaboration | 1 Purdue University, 2 Indiana University, 3 Amazon |
| Pseudocode | Yes | Algorithm 1: GETF |
| Open Source Code | Yes | Code can be accessed at https://github.com/clwan/GETF |
| Open Datasets | Yes | We applied GETF on two real-world datasets, the Chicago crime record data2, and a breast cancer spatial-transcriptomics data3, which represents two scenarios with relatively lower and higher noise. 2Chicago crime records downloaded on March 1st, 2020 from https://data.cityofchicago.org/Public-Safety 3Breast cancer data is retrieved from https://www.spatialresearch.org/resources-published-datasets |
| Dataset Splits | No | The paper refers to evaluating performance metrics like reconstruction error on synthetic and real-world datasets, but does not explicitly detail specific training, validation, or test dataset splits or cross-validation setups. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models, memory, or cloud instance types used for running experiments. |
| Software Dependencies | No | The paper does not provide specific software dependency details with version numbers (e.g., programming languages, libraries, or solvers). |
| Experiment Setup | Yes | Under each scenario, we fixed the number of true patterns as 5 and set the convergence criteria as 1) 10 patterns have been identified, 2) the cost function stopped decreasing with newly identified patterns. Detailed experiment setup is listed in APPENDIX section 4. |