Search Results for author:
Found 7 papers, 2 papers with code
Shape-Tailored Deep Neural Networks With PDEs
ST-DNN are deep networks formulated through the use of partial differential equations (PDE) to be defined on arbitrarily shaped regions.
Supply Chain Digital Twin Framework Design: An Approach of Supply Chain Operations Reference Model and System of Systems
This paper explains what is 'twined' in supply chain digital twin and how to 'twin' them to handle the spatio-temporal dynamic issue.
Class-Agnostic Segmentation Loss and Its Application to Salient Object Detection and Segmentation
For low-fidelity training data (incorrect class label) class-agnostic segmentation loss outperforms the state-of-the-art methods on salient object detection datasets by staggering margins of around 50%.
Shape-Tailored Deep Neural Networks
We stack multiple PDE layers to generalize a deep CNN to arbitrary regions, and apply it to segmentation.
Shape-Tailored Deep Neural Networks Using PDEs for Segmentation
We present Shape-Tailored Deep Neural Networks (ST-DNN).
Digital Twins: State of the Art Theory and Practice, Challenges, and Open Research Questions
This work explores the various DT features and current approaches, the shortcomings and reasons behind the delay in the implementation and adoption of digital twin.
Class-Agnostic Segmentation Loss and Its Application to Salient Object Detection and Segmentation
For low-fidelity training data (incorrect class label) class-agnostic segmentation loss outperforms the state-of-the-art methods on salient object detection datasets by staggering margins of around 50%.
