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Found 4 papers, 1 papers with code
On Efficient Uncertainty Estimation for Resource-Constrained Mobile Applications
Deep neural networks have shown great success in prediction quality while reliable and robust uncertainty estimation remains a challenge.
Towards Efficient Point Cloud Graph Neural Networks Through Architectural Simplification
In recent years graph neural network (GNN)-based approaches have become a popular strategy for processing point cloud data, regularly achieving state-of-the-art performance on a variety of tasks.
Stochastic-Shield: A Probabilistic Approach Towards Training-Free Adversarial Defense in Quantized CNNs
Quantized neural networks (NN) are the common standard to efficiently deploy deep learning models on tiny hardware platforms.
Stochastic-YOLO: Efficient Probabilistic Object Detection under Dataset Shifts
In this paper, we adapt the well-established YOLOv3 architecture to generate uncertainty estimations by introducing stochasticity in the form of Monte Carlo Dropout (MC-Drop), and evaluate it across different levels of dataset shift.
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