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..
Random Projections with Asymmetric Quantization
Authors: Xiaoyun Li, Ping Li
NeurIPS 2019 | Venue PDF | 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 EMAIL; Ping Li Cognitive Computing Lab Baidu Research USA Bellevue, WA 98004 EMAIL |
| 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. |