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.
Chi-square Generative Adversarial Network
Authors: Chenyang Tao, Liqun Chen, Ricardo Henao, Jianfeng Feng, Lawrence Carin Duke
ICML 2018 | Venue PDF | LLM Run Details | Input Tokens: 18,958 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,143 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 | Experiments show that the proposed procedure improves stability and convergence, and yields state-of-art results on a wide range of generative modeling tasks. |
| Researcher Affiliation | Academia | 1Electrical & Computer Engineering, Duke University, Durham, NC 27708, USA 2ISTBI, Fudan University, Shanghai, China. |
| Pseudocode | Yes | Algorithm 1 χ2 GAN. Input: data {xi}, batchsize b, decay ρ, learning rate δ. for t = 1, 2, 3, . . . do... |
| Open Source Code | Yes | Details of the experimental setup are in the SM, and code for our experiments are available from https://www.github.com/ chenyang-tao/chi2gan. |
| Open Datasets | Yes | MNIST We used the binarized MNIST in this experiment and compared with the results from prior results in Table 1. |
| Dataset Splits | No | The paper mentions training and testing on standard datasets (e.g., MNIST, CIFAR-10), but it does not provide specific details on the training, validation, and test splits (e.g., exact percentages or sample counts). |
| Hardware Specification | Yes | All experiments are implemented with Tensorflow and run on a single NVIDIA TITAN X GPU. |
| Software Dependencies | No | All experiments are implemented with Tensorflow and run on a single NVIDIA TITAN X GPU. |
| Experiment Setup | No | In all experiments we have used Xaiver initialization and Adam optimizer. |