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..
Symbolic Graph Reasoning Meets Convolutions
Authors: Xiaodan Liang, Zhiting Hu, Hao Zhang, Liang Lin, Eric P. Xing
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show incorporating SGR significantly improves plain Conv Nets on three semantic segmentation tasks and one image classification task. |
| Researcher Affiliation | Collaboration | School of Intelligent Systems Engineering, Sun Yat-sen University 2Carnegie Mellon University 3 School of Data and Computer Science, Sun Yat-sen University 4Petuum Inc. |
| Pseudocode | No | The paper includes a diagram in Figure 2 showing 'Implementation details of one SGR layer' with boxes and operations, but it does not contain a formal pseudocode block or a section explicitly labeled 'Algorithm'. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code for the described methodology or a link to a code repository. |
| Open Datasets | Yes | We evaluate on three public benchmarks... Specifically, Coco-Stuff [4]... ADE20k [52]... PASCAL-Context [34]... We further conduct studies for image classification task on CIFAR-100 [21]. |
| Dataset Splits | Yes | Coco-Stuff [4]... including 9,000 for training and 1,000 for testing. ADE20k [52] consists of 20,210 images for training and 2,000 for validation... PASCAL-Context [34] includes 4,998 images for training and 5105 for testing... CIFAR-100 [21] consisting of 50K training images and 10K test images. |
| Hardware Specification | Yes | We conduct all experiments using Pytorch, 2 GTX TITAN X 12GB cards on a single server. |
| Software Dependencies | No | The paper states 'We conduct all experiments using Pytorch,' but it does not specify the version number of Pytorch or any other software dependencies. |
| Experiment Setup | Yes | Dl and Dc for feature dimensions... are thus set as 256... We adopt the standard SGD optimization... set the base learning rate to 2.5e-3 for newly initialized layers and 2.5e-4 for pretrained layers. We train 64 epochs for Coco-Stuff and PASCAL-Context, and 120 epochs for ADE20K dataset... the batch size is used as 6. The input crop size is set as 513 513. For CIFAR-100: We set Dl and Dc as 128. During training, we use a mini-batch size of 64 on two GPUs using a cosine learning rate scheduling [16] for 600 epochs. |