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..
Scalable Graph Hashing with Feature Transformation
Authors: Qing-Yuan Jiang, Wu-Jun Li
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on two datasets with one million data points show that our SGH method can outperform the state-of-the-art methods in terms of both accuracy and scalability. |
| Researcher Affiliation | Academia | Qing-Yuan Jiang and Wu-Jun Li National Key Laboratory for Novel Software Technology Collaborative Innovation Center of Novel Software Technology and Industrialization Department of Computer Science and Technology, Nanjing University, China EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Sequential learning algorithm for SGH |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of the described methodology. |
| Open Datasets | Yes | We evaluate our method on two widely used large-scale benchmark datasets: TINY-1M [Liu et al., 2014] and MIRFLICKR-1M [Huiskes et al., 2010]. |
| Dataset Splits | No | For each dataset, we randomly select 5000 data points to construct the test (query) set and the remaining points will be used for training. No explicit mention of a separate validation set or validation split percentages/counts was found. |
| Hardware Specification | Yes | All the experiments are conducted on a workstation with Intel (R) CPU E5-2620V2@2.1G 12 cores and 64G RAM. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., programming languages, libraries, or frameworks with their respective versions) that were used in the experiments. |
| Experiment Setup | Yes | For kernel feature construction, we use Gaussian kernel and take 300 randomly sampled points as kernel bases for our method. We set the parameter ρ = 2 in P(X) and Q(X). Here, γ is a very small positive number to avoid numerical problems, which is 10 6 in our experiments. |