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..
Sign-Full Random Projections
Authors: Ping Li4205-4212
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | A Simulation Study We provide a simulation study to verify the theoretical properties of the four estimators for sign-full random projections: ˆρg, ˆρg,n, ˆρs, ˆρs,n, as well as ˆρ1 for sign-sign projections. An Experimental Study To further verify the theoretical results, we conduct an experimental study on the ranking task for near-neighbor search on 4 public datasets (see Table 1 and Figure 5). |
| Researcher Affiliation | Industry | Ping Li Cognitive Computing Lab (CCL) Baidu Research USA Bellevue, WA 98004, USA EMAIL |
| Pseudocode | No | The paper includes mathematical formulas and derivations but does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating the release of open-source code for the described methodology. |
| Open Datasets | Yes | We conduct an experimental study on the ranking task for near-neighbor search on 4 public datasets (see Table 1 and Figure 5). These four datasets are downloaded from either the UCI repository or the LIBSVM website. Table 1: Information about the datasets Dataset # Train # Query # Dim MNIST 10,000 10,000 780 RCV1 10,000 10,000 47,236 Youtube Audio 10,000 11,930 2,000 Youtube Description 10,000 11,743 12,183,626 |
| Dataset Splits | No | The paper mentions 'training samples' and a 'query set' but does not provide specific percentages or counts for training, validation, and test splits, nor does it refer to standard predefined splits with citations for reproducibility. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware used for running the experiments or simulations. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers for reproducibility. |
| Experiment Setup | Yes | Figure 6 presents the results for the RCV1 datasets, for ρ0 {0.9, 0.8, 0.6}, and for k {50, 100}. |