Search Results for author:
Found 4 papers, 0 papers with code
ResBuilder: Automated Learning of Depth with Residual Structures
In this work, we develop a neural architecture search algorithm, termed Resbuilder, that develops ResNet architectures from scratch that achieve high accuracy at moderate computational cost.
Risk stratification of malignant melanoma using neural networks
In order to improve the detection and classification of malignant melanoma, this paper describes an image-based method that can achieve AUROC values of up to 0. 78 without additional clinical information.
Who breaks early, looses: goal oriented training of deep neural networks based on port Hamiltonian dynamics
The highly structured energy landscape of the loss as a function of parameters for deep neural networks makes it necessary to use sophisticated optimization strategies in order to discover (local) minima that guarantee reasonable performance.
Uncertainty Quantification and Resource-Demanding Computer Vision Applications of Deep Learning
Training deep neural networks is already resource demanding and so is also their uncertainty quantification.
