110 citations · 114 across the 4 of their papers we have counts for
7 papers
Deep Imbalanced Multi-Target Regression: 3D Point Cloud Voxel Content Estimation in Simulated Forests
Amirhossein Hassanzadeh, Bartosz Krawczyk, Michael Saunders +4
Voxelization is an effective approach to reduce the computational cost of processing Light Detection and Ranging (LiDAR) data, yet it results in a loss of fine-scale structural inf…
Uncertainty Quantification In Surface Landmines and UXO Classification Using MC Dropout
Sagar Lekhak, Emmett J. Ientilucci, Dimah Dera +1
Detecting surface landmines and unexploded ordnances (UXOs) using deep learning has shown promise in humanitarian demining. However, deterministic neural networks can be vulnerable…
Through the Perspective of LiDAR: A Feature-Enriched and Uncertainty-Aware Annotation Pipeline for Terrestrial Point Cloud Segmentation
Fei Zhang, Rob Chancia, Josie Clapp +4
Accurate semantic segmentation of terrestrial laser scanning (TLS) point clouds is limited by costly manual annotation. We propose a semi-automated, uncertainty-aware pipeline that…
Robust Learning via Ensemble Density Propagation in Deep Neural Networks
Giuseppina Carannante, Dimah Dera, Ghulam Rasool +2
Learning in uncertain, noisy, or adversarial environments is a challenging task for deep neural networks (DNNs). We propose a new theoretically grounded and efficient approach for…
Self-Compression in Bayesian Neural Networks
Giuseppina Carannante, Dimah Dera, Ghulam Rasool +1
Machine learning models have achieved human-level performance on various tasks. This success comes at a high cost of computation and storage overhead, which makes machine learning…
SUPER-Net: Trustworthy Image Segmentation via Uncertainty Propagation in Encoder-Decoder Networks
Giuseppina Carannante, Nidhal C. Bouaynaya, Dimah Dera +2
Deep Learning (DL) holds great promise in reshaping the industry owing to its precision, efficiency, and objectivity. However, the brittleness of DL models to noisy and out-of-dist…
Robust Explainability: A Tutorial on Gradient-Based Attribution Methods for Deep Neural Networks
Ian E. Nielsen, Dimah Dera, Ghulam Rasool +2
With the rise of deep neural networks, the challenge of explaining the predictions of these networks has become increasingly recognized. While many methods for explaining the decis…