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..
Factor Graph Neural Networks
Authors: Zhen Zhang, Fan Wu, Wee Sun Lee
NeurIPS 2020 | Venue PDF | 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 ο¬le). 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. |