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.
A-NICE-MC: Adversarial Training for MCMC
Authors: Jiaming Song, Shengjia Zhao, Stefano Ermon
NeurIPS 2017 | Venue PDF | LLM Run Details | Input Tokens: 16,272 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 6,770 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 | Empirical results demonstrate that A-NICE-MC combines the strong guarantees of MCMC with the expressiveness of deep neural networks, and is able to significantly outperform competing methods such as Hamiltonian Monte Carlo. |
| Researcher Affiliation | Academia | Jiaming Song Stanford University EMAIL Shengjia Zhao Stanford University EMAIL Stefano Ermon Stanford University EMAIL |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | Yes | Code for reproducing the experiments is available at https://github.com/ermongroup/a-nice-mc. |
| Open Datasets | Yes | We experiment with a distribution pd over images, such as digits (MNIST) and faces (Celeb A). |
| Dataset Splits | No | The paper mentions training and testing, but does not explicitly provide details for validation dataset splits or specific use of a validation set for hyperparameter tuning. |
| Hardware Specification | No | The paper states 'All training and sampling are done on a single CPU thread' but does not specify the CPU model, type, or other hardware details. |
| Software Dependencies | No | The paper mentions 'TensorFlow [38]' and 'Adam [39] as optimizer' but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | All the models are trained with the gradient penalty objective for Wasserstein GANs [17, 15], where λ = 1/3, B = 4 and M = 3. |