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Found 9 papers, 1 papers with code
XTSFormer: Cross-Temporal-Scale Transformer for Irregular Time Event Prediction
Event prediction aims to forecast the time and type of a future event based on a historical event sequence.
Spatial Knowledge-Infused Hierarchical Learning: An Application in Flood Mapping on Earth Imagery
The problem is challenging due to the sparse and noisy input labels, spatial uncertainty within the label inference process, and high computational costs associated with a large number of sample locations.
Uncertainty Quantification of Deep Learning for Spatiotemporal Data: Challenges and Opportunities
With the advancement of GPS, remote sensing, and computational simulations, large amounts of geospatial and spatiotemporal data are being collected at an increasing speed.
A Comprehensive Survey on Uncertainty Quantification for Deep Learning
To fill the gap, this paper presents a systematic taxonomy of UQ methods for DNNs based on the types of uncertainty sources (data uncertainty versus model uncertainty).
An Explainer for Temporal Graph Neural Networks
Given a time series on a graph to be explained, the framework can identify dominant explanations in the form of a probabilistic graphical model in a time period.
Deep Learning for Earth Image Segmentation based on Imperfect Polyline Labels with Annotation Errors
The performance of these models heavily relies on high-quality ground truth segment labels.
Semi-supervised Learning with the EM Algorithm: A Comparative Study between Unstructured and Structured Prediction
Semi-supervised learning aims to learn prediction models from both labeled and unlabeled samples.
Spatial Classification With Limited Observations Based On Physics-Aware Structural Constraint
These methods, however, assume that incomplete feature observations only happen on a small subset of samples, and thus cannot solve problems where the vast majority of samples have missing feature observations.
Deep Neural Network for 3D Surface Segmentation based on Contour Tree Hierarchy
In contrast, we propose to represent surface topological structure by a contour tree skeleton, which is a polytree capturing the evolution of surface contours at different elevation levels.
