Most ML approaches focus on generalization performance on unseen data that are similar to the training data (In-Distribution, or IND).
Advancing probabilistic solar forecasting methods is essential to supporting the integration of solar energy into the electricity grid.
Calibrated uncertainty estimates in machine learning are crucial to many fields such as autonomous vehicles, medicine, and weather and climate forecasting.
NGBoost generalizes gradient boosting to probabilistic regression by treating the parameters of the conditional distribution as targets for a multiparameter boosting algorithm.
Identifying patients who will be discharged within 24 hours can improve hospital resource management and quality of care.
Probabilistic survival predictions from models trained with Maximum Likelihood Estimation (MLE) can have high, and sometimes unacceptably high variance.
The algorithm is a Deep Neural Network trained on the EHR data from previous years, to predict all-cause 3-12 month mortality of patients as a proxy for patients that could benefit from palliative care.
Motivated by these issues, we present a neural network-based approach to language correction.