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.
Product Quantized Translation for Fast Nearest Neighbor Search
Authors: Yoonho Hwang, Mooyeol Baek, Saehoon Kim, Bohyung Han, Hee-Kap Ahn
AAAI 2018 | Venue PDF | LLM Run Details | Input Tokens: 14,387 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 6,688 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 | Although our framework is composed of simple operations only, it achieves the state-of-the-art performance compared to existing nearest neighbor search techniques, which is illustrated quantitatively using various large-scale benchmark datasets in different sizes and dimensions. |
| Researcher Affiliation | Collaboration | Yoonho Hwang, Mooyeol Baek, Saehoon Kim, Bohyung Han, Hee-Kap Ahn Dept. of Computer Science and Engineering POSTECH, Korea EMAIL S.Kim is also affiliated with AItrics. |
| Pseudocode | Yes | The pseudocode of our algorithm is presented in Algorithm 1. |
| Open Source Code | No | The paper states 'We use the source codes released by authors for the implementations of the external algorithms', but it does not provide any explicit statement or link indicating that the source code for their own proposed methodology is publicly available. |
| Open Datasets | Yes | We perform the experiments on four independent datasets, which are denoted by MNIST (Lecun et al. 1998), SIFT5M (J egou, Douze, and Schmid 2011), GIST1M (J egou, Douze, and Schmid 2011), and MSCOCO (Lin et al. 2014). |
| Dataset Splits | No | The paper states that MS-COCO 'contains 4,096-dimensional vectors, which are feature descriptors for 123,287 images in training and validation sets', but it does not specify explicit training/validation/test split percentages, sample counts, or the methodology for data partitioning needed to reproduce the experiment across all datasets or how the validation set was utilized in this specific experimental setup. |
| Hardware Specification | Yes | All tested algorithms are implemented in C++ in Linux (Fedora 21, g++ 4.9.2), specifically using a single core on Intel Core i7-5820k@3.30Ghz with 64GB main memory. |
| Software Dependencies | Yes | All tested algorithms are implemented in C++ in Linux (Fedora 21, g++ 4.9.2), specifically using a single core on Intel Core i7-5820k@3.30Ghz with 64GB main memory. |
| Experiment Setup | Yes | By default, the number of clusters and the dimensionality of partitions are set to 64 and 32, respectively. |