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 [1].
Deep Generative Model for Periodic Graphs
Authors: Shiyu Wang, Xiaojie Guo, Liang Zhao
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experimental evaluations have been conducted to demonstrate the effectiveness of the proposed method. We conducted extensive experiments to demonstrate the effectiveness and efficiency of the proposed methods with multiple real-world and synthetic datasets. |
| Researcher Affiliation | Collaboration | Shiyu Wang Emory University EMAIL Xiaojie Guo IBM Thomas J. Watson Research Center EMAIL Liang Zhao Emory University EMAIL |
| Pseudocode | No | No pseudocode or algorithm blocks are explicitly provided in the paper. |
| Open Source Code | Yes | The code of PGD-VAE is available at https://github.com/shi-yu-wang/PGD-VAE. |
| Open Datasets | Yes | QMOF is a publicaly available database of computed quantum-chemical properties and molecular structures [50]. Mesh Seg contains 380 meshes for quantitative analysis of how people decompose objects into parts and for comparison of mesh segmentation algorithms [11]. |
| Dataset Splits | No | The paper mentions using datasets for training but does not provide specific details on train/validation/test splits (e.g., percentages or sample counts). |
| Hardware Specification | Yes | All experiments were conducted on the 64-bit machine with an NVIDIA GPU, NVIDIA Ge Force RTX 3090. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, CUDA versions). It states "The implementation details of PGD-VAE are presented in Appendix ??", but the appendix content is not provided. |
| Experiment Setup | No | The paper states that "The implementation details of PGD-VAE are presented in Appendix ??," and comparison models use "default settings." No specific hyperparameters or system-level training settings are detailed in the main text. |