no code implementations • 25 Nov 2019 • 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.
no code implementations • 25 Sep 2019 • 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.
no code implementations • ICLR 2019 • Mahdieh Abbasi, Arezoo Rajabi, Azadeh Sadat Mozafari, Rakesh B. Bobba, Christian Gagné
As an appropriate training set for the extra class, we introduce two resources that are computationally efficient to obtain: a representative natural out-distribution set and interpolated in-distribution samples.
no code implementations • 1 May 2019 • Azadeh Sadat Mozafari, Hugo Siqueira Gomes, Wilson Leão, Christian Gagné
The great performances of deep learning are undeniable, with impressive results over a wide range of tasks.
no code implementations • 27 Oct 2018 • Azadeh Sadat Mozafari, Hugo Siqueira Gomes, Wilson Leão, Steeven Janny, Christian Gagné
Temperature Scaling (TS) is a state-of-the-art among measure-based calibration methods which has low time and memory complexity as well as effectiveness.
no code implementations • 21 Aug 2018 • Mahdieh Abbasi, Arezoo Rajabi, Azadeh Sadat Mozafari, Rakesh B. Bobba, Christian Gagne
As an appropriate training set for the extra class, we introduce two resources that are computationally efficient to obtain: a representative natural out-distribution set and interpolated in-distribution samples.
no code implementations • 22 Feb 2018 • Changjian Shui, Azadeh Sadat Mozafari, Jonathan Marek, Ihsen Hedhli, Christian Gagné
Calibrating the confidence of supervised learning models is important for a variety of contexts where the certainty over predictions should be reliable.