ACFlow: Flow Models for Arbitrary Conditional Likelihoods
Authors: Yang Li, Shoaib Akbar, Junier Oliva
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive empirical evaluations show that our model achieves state-of-the-art performance in modeling arbitrary conditional distributions in addition to both single and multiple imputation in synthetic and real-world datasets. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of North Carolina at Chapel Hill, NC, USA 2Department of Mathematics, North Carolina State University, NC, USA. |
| Pseudocode | No | The paper describes mathematical formulations and transformations but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/lupalab/ACFlow. |
| Open Datasets | Yes | We evaluate our method on three common image datasets: MNIST, Omniglot and Celeb A. We use UCI repository datasets preprocessed as described in (Papamakarios et al., 2017). |
| Dataset Splits | No | The paper mentions "Validation likelihoods are used to select the best model" but does not provide specific details on the dataset split used for validation (e.g., percentages, sample counts, or the methodology for creating the splits). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments (e.g., GPU/CPU models, memory specifications). |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., programming languages, libraries, or frameworks with their respective versions). |
| Experiment Setup | No | While the paper mentions that "The details about training procedure are provided in Appendix B" and "Implementation details of ACFlow and baselines are provided in Appendix C.2", these appendices are not included in the provided text. Therefore, specific hyperparameter values or system-level training settings are not present in the main body of the paper. |