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..
Dynamic Hypergraph Structure Learning
Authors: Zizhao Zhang, Haojie Lin, Yue Gao
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on four public datasets show better performance compared with the state-of-the-art methods. |
| Researcher Affiliation | Academia | Zizhao Zhang, Haojie Lin, Yue Gao BNRist, KLISS, School of Software, Tsinghua University, China. EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Dynamic Hypergraph Structure Learning Input: training data set Q, testing data set D, maximal iteration k, and parameters β and λ Output: label projection matrix F and hypergraph structure H 1: Construct the initial hypergraph G = (V, E, W). 2: Initialize the learning rate α. 3: for i = 0 k 1 do 4: Fix H, and update F by Eq. (6). 5: Fix F, and update H by Eq. (8). 6: end for 7: return F, H |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-sourcing the code for the described methodology. |
| Open Datasets | Yes | To evaluate the performance of the proposed method on 3D shape recognition, we have conducted experiments on the National Taiwan University 3D shape dataset (NTU) [Chen et al., 2003] and the Engineering Shape Benchmark (ESB) [Jayanti et al., 2006]. To validate the proposed DHSL method on gesture recognition, we have conducted experiments on the MSR gesture 3D dataset (MSRGesture3D) [Wang et al., 2012] and a hand gesture dataset collected by Huazhong University of Science and Technology (Gesture3DMotion). |
| Dataset Splits | No | The paper describes training and testing data selection processes, but does not explicitly mention a separate validation set or split, nor does it specify how models were selected if no validation set was used. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers required to replicate the experiments. |
| Experiment Setup | Yes | In these experiments, we empirically set the parameter β and λ as 10 and 1 on both datasets, respectively. |