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Found 5 papers, 3 papers with code
UniTS: Building a Unified Time Series Model
However, current foundation models apply to sequence data but not to time series, which present unique challenges due to the inherent diverse and multidomain time series datasets, diverging task specifications across forecasting, classification and other types of tasks, and the apparent need for task-specialized models.
Graph AI in Medicine
With diverse data -- from patient records to imaging -- GNNs process data holistically by viewing modalities as nodes interconnected by their relationships.
Domain Adaptation for Time Series Under Feature and Label Shifts
Additionally, the label distributions of tasks in the source and target domains can differ significantly, posing difficulties in addressing label shifts and recognizing labels unique to the target domain.
Deep Learning for Reference-Free Geolocation for Poplar Trees
A core task in precision agriculture is the identification of climatic and ecological conditions that are advantageous for a given crop.
Evaluating Explainability for Graph Neural Networks
As post hoc explanations are increasingly used to understand the behavior of graph neural networks (GNNs), it becomes crucial to evaluate the quality and reliability of GNN explanations.
