9 citations · 11 across the 2 of their papers we have counts for
3 papers
Encoding the latent posterior of Bayesian Neural Networks for uncertainty quantification
Gianni Franchi, Andrei Bursuc, Emanuel Aldea +2
Bayesian neural networks (BNNs) have been long considered an ideal, yet unscalable solution for improving the robustness and the predictive uncertainty of deep neural networks. Whi…
One Versus all for deep Neural Network Incertitude (OVNNI) quantification
Gianni Franchi, Andrei Bursuc, Emanuel Aldea +2
Deep neural networks (DNNs) are powerful learning models yet their results are not always reliable. This is due to the fact that modern DNNs are usually uncalibrated and we cannot…
TRADI: Tracking deep neural network weight distributions for uncertainty estimation
Gianni Franchi, Andrei Bursuc, Emanuel Aldea +2
During training, the weights of a Deep Neural Network (DNN) are optimized from a random initialization towards a nearly optimum value minimizing a loss function. Only this final st…