A-NICE-MC: Adversarial Training for MCMC
Authors: Jiaming Song, Shengjia Zhao, Stefano Ermon
NeurIPS 2017 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| 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. |