Uncertainty quantification of machine learning models: on conformal prediction
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abstract
Prediction intervals which describe the reliability of the predictive performance of machine learning models are important to guide decision making and to improve trust in deep learning and other forms of machine learning and artificial intelligence. Conformal prediction is a relatively recent, increasingly popular, rigorously proven and simple methodology to address this need for both classification and regression problems, and it does not use distributional assumptions like Gaussianity or the Bayesian framework; one new variant combines it with another technique to generate prediction intervals known as quantile regression. We will illustrate the strengths and limitations of different conformal prediction procedures for a regression problem involving line edge roughness (LER) estimation; LER affects semiconductor device performance and the yield of the manufacturing process. Low-dose images from the scanning electron microscope (SEM) are often used for roughness measurements because of relatively small acquisition times and resist shrinkage, but such images are corrupted by noise, blur, edge effects and other instrument errors. We consider prediction intervals for the deep convolutional neural network EDGENet, which was trained on a large dataset of simulated SEM images and directly estimates the edge positions from a SEM rough line image containing an unknown level of Poisson noise.
