17 citations · 25 across the 4 of their papers we have counts for
4 papers
Meta Learning Black-Box Population-Based Optimizers
Hugo Siqueira Gomes, Benjamin Léger, Christian Gagné
The no free lunch theorem states that no model is better suited to every problem. A question that arises from this is how to design methods that propose optimizers tailored to spec…
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…