Arbitrary Conditional Distributions with Energy
Authors: Ryan Strauss, Junier B. Oliva
NeurIPS 2021 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate ACE on benchmark datasets and show that it outperforms current methods for arbitrary conditional/marginal density estimation. ACE remains effective when trained on data with missing values, making it applicable to real-world datasets that are often incomplete, and we find that ACE scales well to high-dimensional data. Also, unlike some prior methods (e.g., [20]), ACE can naturally model data with both continuous and discrete values. |
| Researcher Affiliation | Academia | Ryan R. Strauss Department of Computer Science UNC at Chapel Hill Chapel Hill, NC 27514 EMAIL Junier B. Oliva Department of Computer Science UNC at Chapel Hill Chapel Hill, NC 27514 EMAIL |
| Pseudocode | Yes | The pseudocode for this procedure is presented in the Appendix (see Algorithm 1). |
| Open Source Code | Yes | An implementation of ACE is available at https://github.com/lupalab/ace. |
| Open Datasets | Yes | We first evaluate ACE on real-valued tabular data. Specifically, we consider the benchmark UCI repository datasets described by Papamakarios et al. [25] (see Table 6 in the Appendix). ... The processed data has 6 continuous features and 8 discrete features and is split into train, validation, and test partitions of size 22003, 5501, and 13788 respectively. |
| Dataset Splits | Yes | The processed data has 6 continuous features and 8 discrete features and is split into train, validation, and test partitions of size 22003, 5501, and 13788 respectively. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as GPU models, CPU types, or cloud instance specifications. |
| Software Dependencies | No | The paper does not provide specific version numbers for ancillary software dependencies or libraries, only mentioning the optimizer Adam. |
| Experiment Setup | No | Full experimental details and hyperparameters can be found in the Appendix. |