Cost-aware Graph Generation: A Deep Bayesian Optimization Approach
Authors: Jiaxu Cui, Bo Yang, Bingyi Sun, Jiming Liu7142-7150
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Intensive experiments conducted on two challenging real-world applications, including molecular discovery and neural architecture search, demonstrate its effectiveness and applicability. The results show that it can generate the optimal graphs and reduce the evaluation costs significantly compared to the state-of-the-art. |
| Researcher Affiliation | Academia | Jiaxu Cui,1,2 Bo Yang,1,2 Bingyi Sun,1,2,4 Jiming Liu 3 1College of Computer Science and Technology, Jilin University, China 2Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, China 3Department of Computer Science, Hong Kong Baptist University, Hong Kong 4National Laboratory for Parallel and Distributed Processing, National University of Defense Technology, China |
| Pseudocode | Yes | Algorithm 1 Procedure of the CAGG |
| Open Source Code | Yes | Code available at: https://github.com/csjtx1021/CAGG |
| Open Datasets | Yes | QM9 (Ramakrishnan et al. 2014) is a benchmark dataset in quantum chemistry, which contains about 134K molecules with at most 9 heavy atoms. NASBench201 (Dong and Yang 2020) is a unified benchmark for most up-to-date cell-based NAS methods. Indoor (Torres-Sospedra et al. 2014) collects about 20K localization records of mobile devices. Slice (Graf et al. 2011) contains 53,500 CT images from 74 patients. |
| Dataset Splits | No | The paper mentions datasets used but does not explicitly state the specific training, validation, or test dataset splits (e.g., percentages or exact counts) for its experiments. While NASBench201 is a benchmark with predefined splits, the paper does not specify how they used these or other datasets for their own partitioning. |
| Hardware Specification | Yes | Google Cloud Platform cost is calculated based on the price of on-demand c2-standard-8 instances. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies, such as programming languages, libraries, or frameworks used for implementation. |
| Experiment Setup | No | The paper mentions overall budgets for experiments (e.g., 'maximum # Eval to 3,000', '12-hour budget') and refers to supplementary material for constraint details. However, it does not provide specific hyperparameters like learning rates, batch sizes, optimizers, or detailed training configurations in the main text. |