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.
Generative Ratio Matching Networks
Authors: Akash Srivastava, Kai Xu, Michael U. Gutmann, Charles Sutton
ICLR 2020 | Venue PDF | LLM Run Details | Input Tokens: 19,488 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,680 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 | In this section we empirically compare GRAM-nets against MMD-GANs and vanilla GANs, on the Cifar10 and Celeb A image datasets. |
| Researcher Affiliation | Collaboration | Akash Srivastava MIT-IBM Watson AI Lab EMAIL Michael U. Gutmann University of Edinburgh EMAIL Kai Xu University of Edinburgh EMAIL Charles Sutton Google AI EMAIL |
| Pseudocode | Yes | Algorithm 1: Generative ratio matching |
| Open Source Code | Yes | 1Official implementations are available at https://github.com/GRAM-nets. |
| Open Datasets | Yes | In this section we empirically compare GRAM-nets against MMD-GANs and vanilla GANs, on the Cifar10 and Celeb A image datasets. |
| Dataset Splits | No | The paper states that FID is reported on a 'held-out set that was not used to train the models', implying a train/test split, but does not provide specific details on validation splits (e.g., percentages, sample counts) or how the data was partitioned into train, validation, and test sets. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware (e.g., GPU models, CPU types, memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper mentions optimizers like ADAM and RMSprop, and points to an external implementation of MMD-GANs, but it does not specify version numbers for any key software dependencies or libraries required for reproduction. |
| Experiment Setup | Yes | To facilitate fair comparison with MMD-GAN we set all the hyperparameters shared across the three methods to the values used in Li et al. (2017). Therefore, we use a learning rate of 5e 5 and set the batch size to 64. |