3 accepted papers
Output uncertainty indicates whether the probabilistic properties of the overall distribution reflect objective characteristics of the model output. Unlike most loss functions and metrics in machine learning, uncertainty pertains to individual samples, but validating it on individual samples is unfe…
Modern deep learning models often exhibit overconfident predictions, inadequately capturing uncertainty. During model optimization, the expected calibration error tends to overfit earlier than classification accuracy, indicating distinct optimization objectives for classification error and calibrat…
Magnetic microrobots are potentially used in various biomedical applications due to their distinguished properties in bio-related manipulation such as minimally invasive and accessible in complex bio-environment. Precise path tracking control of magnetic microrobots in complex interference is an imp