Abstract: Systems, apparatuses, and methods for training a machine learning (ML) model. Training the ML model may include using contour lines on a plot of prediction values to expected values to determine loss values indicative of errors between prediction values output by the ML model and corresponding expected values. The contour lines may be associated with loss values. Using the contour lines to determine the loss values may include, for each prediction value-expected value pair: generating a one-dimensional loss function through the prediction value-expected value pair, and using the one-dimensional loss function to determine a loss value for the prediction value-expected value pair. Training the ML model may include using an overall loss function to determine an overall loss of the ML model based on the determined loss values. Training the ML model may include adjusting the ML model to minimize the overall loss of the ML model.

Abstract: Systems, apparatuses, and methods for training and/or assessing the stability of a machine learning (ML) model. Training and/or assessing the stability may include, for each input sample n of N input samples, for each perturbation q of Q perturbations: determining a perturbed input sample nq by perturbing the input sample n and using the ML model to obtain a perturbed output yqn based on the perturbed input sample nq. Training and/or assessing the stability may include, for each input sample n of the N input samples, aggregating the perturbed outputs yqn to obtain an aggregate perturbed output yn of the Q perturbations for the input sample n. Training the ML model may include updating one or more parameters of the ML model based on at least the aggregate perturbed outputs yn. Assessing the stability may include aggregating the aggregate perturbed outputs yn (or relative output variations ynrel).