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.
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.
no code implementations • 13 May 2021 • Lorena Qendro, Sangwon Ha, René de Jong, Partha Maji
Quantized neural networks (NN) are the common standard to efficiently deploy deep learning models on tiny hardware platforms.
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.