Abstract: Predicting biomedical interactions is crucial for understanding various biological processes and drug discovery. Graph neural networks (GNNs) are promising in identifying novel interactions ...

Abstract: In this article, we develop generative models that generate embeddings for graph nodes while using only their initial features without any knowledge about their neighborhoods and connections ...

Managing irregular multivariate time series (IMTS) data is a significant challenge due to inherent irregularities and missing values. Recent advancements have utilized graph neural networks (GNNs) to ...

Self-contained workspace for ORB fine-tuning, merging, and evaluation. Safe to zip/upload as-is (e.g., to Kaggle). python scripts/kaggle/merge_mean.py --config models ...

Framework of DynIMTS. The model is a recurrent structure based on a spatial-temporal encoder and consists of three main components: embedding learning, spatial-temporal learning, and graph learning.

Forbes contributors publish independent expert analyses and insights. I track enterprise software application development & data management. Jul 03, 2025, 10:43am EDT Business 3d tablet virtual growth ...

ABSTRACT: Missing data remains a persistent and pervasive challenge across a wide range of domains, significantly impacting data analysis pipelines, predictive modeling outcomes, and the reliability ...

Graph neural networks (GNNs) have gained traction and have been applied to various graph-based data analysis tasks due to their high performance. However, a major concern is their robustness, ...

Develop a new example that illustrates dynamic embeddings. The goal is to show what the workflow and API will look like as we continue our engineering efforts on this problem.