Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Boosting Complementary Hash Tables for Fast Nearest Neighbor Search
Authors: Xianglong Liu, Cheng Deng, Yadong Mu, Zhujin Li
AAAI 2017 | Venue PDF | LLM Run Details | Input Tokens: 16,514 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,734 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments carried out on two popular tasks including Euclidean and semantic nearest neighbor search demonstrate that the proposed boosted complementary hash-tables method enjoys the strong table complementarity and significantly outperforms the state-of-the-arts. |
| Researcher Affiliation | Academia | State Key Lab of Software Development Environment, Beihang University, Beijing 100191, China School of Electronic Engineering, Xidian University, Xi an 710071, Shaanxi, China Institute of Computer Science and Technology, Peking University, Beijing 100080, China |
| Pseudocode | Yes | Algorithm 1 Boosted Complementary Hash-Tables (BCH). |
| Open Source Code | No | No explicit statement or link providing open-source code for the methodology was found in the paper. |
| Open Datasets | Yes | We employ two widely-used large data sets: SIFT1M and GIST-1M1, consisting of one million 128-D SIFT and 960-D GIST features respectively. [...] We employ two widely-used large image datasets: CIFAR-102 and NUS-WIDE3. (1)http://corpus-texmex.irisa.fr (2)https://www.cs.toronto.edu/~kriz/cifar.html (3)lms.comp.nus.edu.sg/research/NUS-WIDE.htm |
| Dataset Splits | No | For each dataset, we construct a training and a testing set respectively with 10,000 and 3,000 random samples. |
| Hardware Specification | Yes | All experiments are conducted on a workstation with Intel Xeon CPU E5-4607@2.60GHz and 48GB memory |
| Software Dependencies | No | No specific software dependencies with version numbers were mentioned in the paper. |
| Experiment Setup | Yes | As to BCH, for each training sample we choose 50 homogenous neighbors and 100 heterogenous neighbors based on Euclidean distance. Moreover, the parameter λ is simply set to 1.0. |