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..
Principled Simplicial Neural Networks for Trajectory Prediction
Authors: T. Mitchell Roddenberry, Nicholas Glaze, Santiago Segarra
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We then demonstrate the effectiveness of this archi tecture in extrapolating trajectories on synthetic and real datasets, with particular emphasis on the gains in generalizability to unseen trajectories. and 6. Experiments |
| Researcher Affiliation | Academia | 1Department of Electrical and Computer Engineering, Rice University, Houston, Texas, USA. |
| Pseudocode | Yes | Algorithm 1 SCo Ne for Trajectory Prediction |
| Open Source Code | Yes | Code available at https: //github.com/nglaze00/SCo Ne_GCN. |
| Open Datasets | Yes | Data available from NOAA/AOML at http://www.aoml. noaa.gov/envids/gld/ and as supplementary material. and Following the example of Schaub et al. (2020), we generate a simplicial complex by drawing 400 points uniformly at random in the unit square, and then apply ing a Delaunay triangulation to obtain a mesh, after which we remove all nodes and edges in two regions, pictured in Fig. 2(a). |
| Dataset Splits | No | For the synthetic dataset, it states: 'We generate 1000 such trajectories for our experiment, using 800 of them for training and 200 for testing.' For the Berlin dataset, it states: 'divided into an 80/20 train/test split.' A distinct validation split is not explicitly provided. |
| Hardware Specification | No | The paper does not provide specific details on the hardware used for running experiments (e.g., GPU/CPU models, memory specifications). |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | In evaluating our proposed architecture for trajectory pre diction, we consider SCo Ne with 3 layers, where each layer has F = 16 hidden features. By default, we use the tanh φ( ) activation function, but we also use Re LU and sigmoid ac tivations to compare. In training SCo Ne, we minimize the cross-entropy between the softmax output z and the ground truth fnal nodes in each batch of training samples. |