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Found 7 papers, 1 papers with code
Building Calibrated Deep Models via Uncertainty Matching with Auxiliary Interval Predictors
With rapid adoption of deep learning in critical applications, the question of when and how much to trust these models often arises, which drives the need to quantify the inherent uncertainties.
Learn-By-Calibrating: Using Calibration as a Training Objective
Calibration error is commonly adopted for evaluating the quality of uncertainty estimators in deep neural networks.
Heteroscedastic Calibration of Uncertainty Estimators in Deep Learning
The role of uncertainty quantification (UQ) in deep learning has become crucial with growing use of predictive models in high-risk applications.
Calibrate and Prune: Improving Reliability of Lottery Tickets Through Prediction Calibration
The hypothesis that sub-network initializations (lottery) exist within the initializations of over-parameterized networks, which when trained in isolation produce highly generalizable models, has led to crucial insights into network initialization and has enabled efficient inferencing.
Calibrating Healthcare AI: Towards Reliable and Interpretable Deep Predictive Models
The wide-spread adoption of representation learning technologies in clinical decision making strongly emphasizes the need for characterizing model reliability and enabling rigorous introspection of model behavior.
Designing Accurate Emulators for Scientific Processes using Calibration-Driven Deep Models
Predictive models that accurately emulate complex scientific processes can achieve exponential speed-ups over numerical simulators or experiments, and at the same time provide surrogates for improving the subsequent analysis.
Ask-n-Learn: Active Learning via Reliable Gradient Representations for Image Classification
Deep predictive models rely on human supervision in the form of labeled training data.
