Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Re-randomized Densification for One Permutation Hashing and Bin-wise Consistent Weighted Sampling
Authors: Ping Li, Xiaoyun Li, Cun-Hui Zhang
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on a range of datasets and tasks conο¬rm the effectiveness of proposed methods. |
| Researcher Affiliation | Collaboration | Ping Li Cognitive Computing Lab Baidu Research Bellevue, WA 98004, USA EMAIL Xiaoyun Li Department of Statistics Rutgers University Piscataway, NJ 08854, USA EMAIL Cun-Hui Zhang Department of Statistics Rutgers University Piscataway, NJ 08854, USA EMAIL |
| Pseudocode | Yes | Algorithm 1: Consistent Weighted Sampling (CWS). |
| Open Source Code | No | The paper does not provide any explicit statement or link regarding the public release of source code for the described methodology. |
| Open Datasets | Yes | This dataset (named Words ) has been used in a few previous papers on hashing and sketching, as early as in 2005 [23]. |
| Dataset Splits | No | The paper mentions evaluating on 'original data' and 'hashed data' and reports 'test accuracy', but does not specify exact train/validation/test split percentages, sample counts, or refer to a standard split with proper citation. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used for the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., 'Python 3.8', 'PyTorch 1.9'). |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as hyperparameter values (e.g., learning rate, batch size) or specific training configurations. |