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.
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 • 22 Feb 2021 • Martin Ferianc, Partha Maji, Matthew Mattina, Miguel Rodrigues
Bayesian neural networks (BNNs) are making significant progress in many research areas where decision-making needs to be accompanied by uncertainty estimation.
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.
no code implementations • 4 Mar 2019 • Partha Maji, Andrew Mundy, Ganesh Dasika, Jesse Beu, Matthew Mattina, Robert Mullins
The Winograd or Cook-Toom class of algorithms help to reduce the overall compute complexity of many modern deep convolutional neural networks (CNNs).