Random Projections with Asymmetric Quantization
Authors: Xiaoyun Li, Ping Li
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on nearest neighbor search justify the theory and illustrate the effectiveness of our proposed estimators. |
| Researcher Affiliation | Collaboration | Xiaoyun Li Department of Statistics Rutgers University Piscataway, NJ 08854 xiaoyun.li@rutgers.edu; Ping Li Cognitive Computing Lab Baidu Research USA Bellevue, WA 98004 liping11@baidu.com |
| Pseudocode | No | No pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | Yes | In this section, we test proposed estimators on 3 datasets from the UCI repository (Table 1) [16]. [16] Dheeru Dua and Casey Graff. UCI machine learning repository, 2017. |
| Dataset Splits | No | The paper mentions testing estimators on datasets but does not provide specific details on data splits (training, validation, or test sets) or cross-validation methodology. |
| Hardware Specification | No | No specific hardware details (GPU/CPU models, memory, etc.) used for running experiments were mentioned in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers were mentioned in the paper. |
| Experiment Setup | No | The paper describes the empirical study and evaluation metrics but does not provide specific experimental setup details, such as hyperparameter values or training configurations. |