Uncertainty Estimation in Deep Neural Networks: A Comparative Study of Bayesian Approximation and Conformal Prediction
Deep neural networks have been increasingly used in various scientific fields due to their versatility and high performance. Despite achieving high classification accuracy, deep learning models can be poorly calibrated, assigning overly confident probabilities to incorrect predictions. This overconfidence highlights the absence of built-in mechanisms for uncertainty quantification. The thesis co
