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Towards Spatially-Lucid AI Classification in Non-Euclidean Space: An Application for MxIF Oncology Data
Given multi-category point sets from different place-types, our goal is to develop a spatially-lucid classifier that can distinguish between two classes based on the arrangements of their points.
Reducing Uncertainty in Sea-level Rise Prediction: A Spatial-variability-aware Approach
Given multi-model ensemble climate projections, the goal is to accurately and reliably predict future sea-level rise while lowering the uncertainty.
A Survey on Solving and Discovering Differential Equations Using Deep Neural Networks
Ordinary and partial differential equations (DE) are used extensively in scientific and mathematical domains to model physical systems.
Towards Comparative Physical Interpretation of Spatial Variability Aware Neural Networks: A Summary of Results
The proposed physical interpretation improves the transparency of SVANN models and the analytical results highlight the trade-off between model transparency and model performance (e. g., F1-score).
Towards Spatial Variability Aware Deep Neural Networks (SVANN): A Summary of Results
Spatial variability has been observed in many geo-phenomena including climatic zones, USDA plant hardiness zones, and terrestrial habitat types (e. g., forest, grasslands, wetlands, and deserts).
