Clinical practitioners use all available data modalities for diagnosing and treating eye diseases like Diabetic Retinopathy (DR) or Diabetic Macular Edema (DME).

A broad spectrum of dynamical systems consists of multiple interacting objects.

Particularly important is the ability to incorporate domain knowledge of invariances, e.g., translational invariance of images. Kernels based on the maximum similarity over a group of transformations are not generally positive definite. Perhaps it is for this reason that they have not been studied theoretically.

Particularly important is the ability to incorporate domain knowledge of invariances, e.g., translational invariance of images. Kernels based on the maximum similarity over a group of transformations are not generally positive definite. Perhaps it is for this reason that they have not been studied theoretically.

Existing explanation methods are often limited to explaining predictions of a pre-specified class, which answers the question "why is the input classified into this class?" However, such explanations with respect to a single class are inherently insufficient because they do not capture features with class-discriminative power. That is, features that are important for predicting one class may also be important for other classes.