no code implementations • 7 May 2024 • Andrea Barucci, Giulia Ciacci, Pietro Liò, Tiago Azevedo, Andrea Di Cencio, Marco Merella, Giovanni Bianucci, Giulia Bosio, Simone Casati, Alberto Collareta
Palaeontology is now observing this trend as well.
no code implementations • 11 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.
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.
no code implementations • 13 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.
1 code implementation • 7 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.
1 code implementation • 29 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.