Optimal Densification for Fast and Accurate Minwise Hashing
Authors: Anshumali Shrivastava
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental evaluations on real sparse and high-dimensional datasets validate our claims. |
| Researcher Affiliation | Academia | 1Rice University, Houston, TX, USA. Correspondence to: Anshumali Shrivastava <anshumali@rice.edu>. |
| Pseudocode | Yes | Algorithm 1 Optimal Densiļ¬cation |
| Open Source Code | Yes | Codes are available at http://rush.rice.edu/ fastest-minwise.html |
| Open Datasets | Yes | we use three publicly available text datasets: 1) RCV1, 2) URL and 3) News20. |
| Dataset Splits | No | The paper does not explicitly provide details about training, validation, and test dataset splits with percentages or sample counts, or refer to predefined splits. |
| Hardware Specification | Yes | All the experiments were done on Intel i7-6500U processor laptop with 16GB RAM. |
| Software Dependencies | No | The paper states methods were implemented in C++ but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | No | The paper describes the hashing schemes and evaluates their performance, but does not provide specific experimental setup details such as hyperparameters or training configurations for a learning model. |