Factor Graph Neural Networks
Authors: Zhen Zhang, Fan Wu, Wee Sun Lee
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on synthetic and real datasets demonstrate the potential of the proposed architecture. |
| Researcher Affiliation | Academia | 1 Australian Institute for Machine Learning & The University of Adelaide, Australia 2 University of Illinois at Urbana-Champaign 3 School of Computing, National University of Singapore |
| Pseudocode | Yes | Algorithm 1 The FGNN layer |
| Open Source Code | No | The paper does not contain an explicit statement about releasing the source code for its methodology or a link to a code repository. |
| Open Datasets | Yes | We train our model on the Human3.6M dataset using the standard training-val-test split as previous works [17, 20, 22] |
| Dataset Splits | Yes | We train our model on the Human3.6M dataset using the standard training-val-test split as previous works [17, 20, 22] |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The model is implemented using pytorch [27], but no specific version number for PyTorch or any other software dependency is provided. |
| Experiment Setup | Yes | In this task, we use a factor graph neural network consisting of 8 FGNN layers (the details is provided in the supplementary file). The model is implemented using pytorch [27], trained with Adam optimizer [12] with initial learning rate lr = 3 10 3 and after each epoch, lr is decreased by a factor of 0.98. |