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Automatically learning hybrid digital twins of dynamical systems
Digital Twins (DTs) are computational models that simulate the states and temporal
dynamics of real-world systems, playing a crucial role in prediction, understanding, and …
dynamics of real-world systems, playing a crucial role in prediction, understanding, and …
Controllable Sequence Editing for Counterfactual Generation
Sequence models generate counterfactuals by modifying parts of a sequence based on a
given condition, enabling reasoning about" what if" scenarios. While these models excel at …
given condition, enabling reasoning about" what if" scenarios. While these models excel at …
No Equations Needed: Learning System Dynamics Without Relying on Closed-Form ODEs
Data-driven modeling of dynamical systems is a crucial area of machine learning. In many
scenarios, a thorough understanding of the model's behavior becomes essential for practical …
scenarios, a thorough understanding of the model's behavior becomes essential for practical …
C-PPT: A Channel-Wise Prototypical Part Transformer for Interpretable Perioperative Complication Prediction with Blood Pressure
J Zhang, X Yang, Y Chen, R Sun - International Conference on Pattern …, 2024 - Springer
Continuous monitoring of multi-channel blood pressure during the perioperative period is
crucial for predicting complications. ProtoPNet has garnered attention as powerful tools for …
crucial for predicting complications. ProtoPNet has garnered attention as powerful tools for …