D-VAE: A Variational Autoencoder for Directed Acyclic Graphs
Authors: Muhan Zhang, Shali Jiang, Zhicheng Cui, Roman Garnett, Yixin Chen
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness of our proposed D-VAE through two tasks: neural architecture search and Bayesian network structure learning. Experiments show that our model not only generates novel and valid DAGs, but also produces a smooth latent space that facilitates searching for DAGs with better performance through Bayesian optimization. |
| Researcher Affiliation | Academia | Muhan Zhang, Shali Jiang, Zhicheng Cui, Roman Garnett, Yixin Chen Department of Computer Science and Engineering Washington University in St. Louis {muhan, jiang.s, z.cui, garnett}@wustl.edu, chen@cse.wustl.edu |
| Pseudocode | No | The paper describes the encoding and decoding procedures using text and illustrative figures, but does not include formal pseudocode blocks or algorithms labeled as such. |
| Open Source Code | Yes | All the code and data are available at https://github.com/muhanzhang/D-VAE. |
| Open Datasets | Yes | Our neural network dataset contains 19,020 neural architectures from the ENAS software [33]. ... on CIFAR-10 [60]. ... Our Bayesian network dataset contains 200,000 random 8-node Bayesian networks from the bnlearn package [61] in R. |
| Dataset Splits | No | We split the dataset into 90% training and 10% held-out test sets. ... We use the training set for VAE training, and use the test set only for evaluation. |
| Hardware Specification | No | The paper does not provide specific hardware details such as CPU/GPU models, memory, or cloud computing instances used for running experiments. |
| Software Dependencies | No | The paper mentions using the 'bnlearn package [61] in R' and 'ENAS software [33]', but does not provide specific version numbers for these software components or other libraries/dependencies. |
| Experiment Setup | No | The paper refers to 'Training details are in Appendix K' for more information, indicating that specific experimental setup details such as hyperparameters are not provided in the main text. |