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Found 5 papers, 3 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.
An Underexplored Dilemma between Confidence and Calibration in Quantized Neural Networks
We show that this robustness can be partially explained by the calibration behavior of modern CNNs, and may be improved with overconfidence.
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-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.
Towards a predictive spatio-temporal representation of brain data
The characterisation of the brain as a "connectome", in which the connections are represented by correlational values across timeseries and as summary measures derived from graph theory analyses, has been very popular in the last years.
