Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Optimal Densification for Fast and Accurate Minwise Hashing
Authors: Anshumali Shrivastava
ICML 2017 | Venue PDF | LLM Run Details | Input Tokens: 18,410 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,607 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Optimal Densification |
| 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. |