Arbitrary Conditional Distributions with Energy
Authors: Ryan Strauss, Junier B. Oliva
NeurIPS 2021 | Conference 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 rrs@cs.unc.edu Junier B. Oliva Department of Computer Science UNC at Chapel Hill Chapel Hill, NC 27514 joliva@cs.unc.edu |
| 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. |