17 citations · 37 across the 6 of their papers we have counts for
6 papers
A Novel Unsupervised Post-Processing Calibration Method for DNNS with Robustness to Domain Shift
Azadeh Sadat Mozafari, Hugo Siqueira Gomes, Christian Gagne
The uncertainty estimation is critical in real-world decision making applications, especially when distributional shift between the training and test data are prevalent. Many calib…
Unsupervised Temperature Scaling: An Unsupervised Post-Processing Calibration Method of Deep Networks
Azadeh Sadat Mozafari, Hugo Siqueira Gomes, Wilson Leão +1
The great performances of deep learning are undeniable, with impressive results over a wide range of tasks. However, the output confidence of these models is usually not well-calib…
Attended Temperature Scaling: A Practical Approach for Calibrating Deep Neural Networks
Azadeh Sadat Mozafari, Hugo Siqueira Gomes, Wilson Leão +2
Recently, Deep Neural Networks (DNNs) have been achieving impressive results on wide range of tasks. However, they suffer from being well-calibrated. In decision-making application…
Controlling Over-generalization and its Effect on Adversarial Examples Generation and Detection
Mahdieh Abbasi, Arezoo Rajabi, Azadeh Sadat Mozafari +2
Convolutional Neural Networks (CNNs) significantly improve the state-of-the-art for many applications, especially in computer vision. However, CNNs still suffer from a tendency to…
Diversity regularization in deep ensembles
Changjian Shui, Azadeh Sadat Mozafari, Jonathan Marek +2
Calibrating the confidence of supervised learning models is important for a variety of contexts where the certainty over predictions should be reliable. However, it has been report…
Node-Adapt, Path-Adapt and Tree-Adapt:Model-Transfer Domain Adaptation for Random Forest
Azadeh S. Mozafari, David Vazquez, Mansour Jamzad +1
Random Forest (RF) is a successful paradigm for learning classifiers due to its ability to learn from large feature spaces and seamlessly integrate multi-class classification, as w…