Search Results for author: Tiago Azevedo

Found 5 papers, 3 papers with code

On Efficient Uncertainty Estimation for Resource-Constrained Mobile Applications

no code implementations11 Nov 2021 Johanna Rock, Tiago Azevedo, René de Jong, Daniel Ruiz-Muñoz, Partha Maji

Deep neural networks have shown great success in prediction quality while reliable and robust uncertainty estimation remains a challenge.

Multi-class Classification Sensor Fusion

An Underexplored Dilemma between Confidence and Calibration in Quantized Neural Networks

1 code implementation NeurIPS Workshop ICBINB 2021 Guoxuan Xia, Sangwon Ha, Tiago Azevedo, Partha Maji

We show that this robustness can be partially explained by the calibration behavior of modern CNNs, and may be improved with overconfidence.

Decision Making Quantization

Towards Efficient Point Cloud Graph Neural Networks Through Architectural Simplification

no code implementations13 Aug 2021 Shyam A. Tailor, René de Jong, Tiago Azevedo, Matthew Mattina, Partha Maji

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.

Mixed Reality

Stochastic-YOLO: Efficient Probabilistic Object Detection under Dataset Shifts

1 code implementation7 Sep 2020 Tiago Azevedo, René de Jong, Matthew Mattina, Partha Maji

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.

Image Classification object-detection +1

Towards a predictive spatio-temporal representation of brain data

1 code implementation29 Feb 2020 Tiago Azevedo, Luca Passamonti, Pietro Liò, Nicola Toschi

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.

Binary Classification

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