Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
D-VAE: A Variational Autoencoder for Directed Acyclic Graphs
Authors: Muhan Zhang, Shali Jiang, Zhicheng Cui, Roman Garnett, Yixin Chen
NeurIPS 2019 | Venue PDF | 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 EMAIL, EMAIL |
| 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. |