Distributional Learning of Variational AutoEncoder: Application to Synthetic Data Generation
Authors: Seunghwan An, Jong-June Jeon
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiments and sections like 4.2 Evaluation Metrics and 4.3 Results with tables (e.g., Table 1: Averaged MLu metrics (MARE, F1).). |
| Researcher Affiliation | Academia | Seunghwan An and Jong-June Jeon Department of Statistical Data Science, University of Seoul, S. Korea {dkstmdghks79, jj.jeon}@uos.ac.kr |
| Pseudocode | Yes | Algorithm 1 Discretization of Estimated CDF |
| Open Source Code | Yes | We release the code at https://github.com/an-seunghwan/DistVAE. |
| Open Datasets | Yes | For evaluation, we consider following real tabular datasets: covertype, credit, loan, adult, cabs, and kings (see Appendix A.8 for detailed data descriptions). ... covertype: https://www.kaggle.com/datasets/uciml/forest-cover-type-dataset |
| Dataset Splits | No | Table 8: Description of datasets. #C represents the number of continuous and ordinal variables. #D denotes the number of discrete variables. Dataset Train/Test Split... covertype 45k/5k (Only train/test splits are specified, not validation). |
| Hardware Specification | Yes | We run all experiments using Geforce RTX 3090 GPU |
| Software Dependencies | No | Our experimental codes are all available with pytorch. (PyTorch version is not specified, and no other software dependencies with versions are listed). |
| Experiment Setup | Yes | Table 9: Hyper-parameter settings for tabular dataset experiments. Model epochs batch size learning rate β (or decoder std range) d M ... Dist VAE 100 256 0.001 0.5 2 10 |