Reliable execution of scientific workflows is a fundamental concern in computational campaigns. Therefore, detecting and diagnosing anomalies are both important and challenging for workflow executions ...

Illustrations of Neural Network architectures are often time-consuming to produce, and machine learning researchers all too often find themselves constructing these diagrams from scratch by hand.

Physics-Informed Neural Networks (PINN) emerged as a powerful tool for solving scientific computing problems, ranging from the solution of Partial Differential Equations to data assimilation tasks.

Graph Neural Networks (GNNs) analyze relational data in text to capture complex structures, while Named Entity Recognition (NER) highlights key entities. Our model offers scalability and efficiency, ...

AppTek announced significant enhancements to its language technologies platform for supporting the needs of the media and entertainment industry.

With the academic release coming soon, the IDEAS platform will include the Peri-Event Analysis Workflow for turnkey secondary analysis of neural circuit activity and user-defined timestamps ...