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Found 5 papers, 0 papers with code
Meta Learning Black-Box Population-Based Optimizers
From that framework's formulation, we propose to parameterize the algorithm using deep recurrent neural networks and use a meta-loss function based on stochastic algorithms' performance to train efficient data-driven optimizers over several related optimization tasks.
A Novel Unsupervised Post-Processing Calibration Method for DNNS with Robustness to Domain Shift
The uncertainty estimation is critical in real-world decision making applications, especially when distributional shift between the training and test data are prevalent.
Unsupervised Temperature Scaling: Robust Post-processing Calibration for Domain Shift
The uncertainty estimation is critical in real-world decision making applications, especially when distributional shift between the training and test data are prevalent.
Unsupervised Temperature Scaling: An Unsupervised Post-Processing Calibration Method of Deep Networks
The great performances of deep learning are undeniable, with impressive results over a wide range of tasks.
Attended Temperature Scaling: A Practical Approach for Calibrating Deep Neural Networks
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.
