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Found 3 papers, 1 papers with code
Counterfactual Image Generation for adversarially robust and interpretable Classifiers
Neural Image Classifiers are effective but inherently hard to interpret and susceptible to adversarial attacks.
Design Space Exploration and Explanation via Conditional Variational Autoencoders in Meta-model-based Conceptual Design of Pedestrian Bridges
For conceptual design, engineers rely on conventional iterative (often manual) techniques.
Multi-Objective Loss Balancing for Physics-Informed Deep Learning
Physics-Informed Neural Networks (PINN) are algorithms from deep learning leveraging physical laws by including partial differential equations together with a respective set of boundary and initial conditions as penalty terms into their loss function.
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