Re-randomized Densification for One Permutation Hashing and Bin-wise Consistent Weighted Sampling
Authors: Ping Li, Xiaoyun Li, Cun-Hui Zhang
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on a range of datasets and tasks confirm the effectiveness of proposed methods. |
| Researcher Affiliation | Collaboration | Ping Li Cognitive Computing Lab Baidu Research Bellevue, WA 98004, USA liping11@baidu.com Xiaoyun Li Department of Statistics Rutgers University Piscataway, NJ 08854, USA xiaoyun.li@rutgers.edu Cun-Hui Zhang Department of Statistics Rutgers University Piscataway, NJ 08854, USA cunhui@stat.rutgers.edu |
| 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. |