Abstract: Training a robust machine learning model by mapping an input data set to a first feature space, applying a transformation to the first feature space, yielding a second feature space, and training a dense model using the first feature space, and the second feature space.

Abstract: Evaluating machine learning model classifications, training a machine learning classification model using a training data set from a first data distribution, determining a classification for test data from a second data distribution using the machine learning classification model, wherein the first data distribution and the second data distribution are disjoint distributions, determining an uncertainty for the classification of the test data according to a difference between the first data distribution and the second data distribution.

Abstract: Training a robust machine learning model by mapping an input data set to a first feature space, applying a transformation to the first feature space, yielding a second feature space, and training a dense model using the first feature space, and the second feature space.

Abstract: According to one or more of the embodiments herein, a subject image of biological tissue is acquired from a pulse sequence of a magnetic resonance imaging (MRI) device, and one or more pulse sequence parameters used to acquire the subject image may be estimated based on a relationship between the subject image and the biological tissue. A new atlas image may then be synthesized using the pulse sequence and the estimated pulse sequence parameters of the subject image, and an intensity transformation between the new atlas image and a desired reference atlas image may be learned. As such, a desired subject image may be synthesized by applying the intensity transformation to the subject image.