Learning to compute inner consensus -- A noble approach to modeling agreement between Capsules

The now called field of Deep Learning has expanded these ideas by creating models that stack multiple layers of Perceptrons. These Multilayer Perceptrons, commonly known as Neural Networks [7], achieve greater representation capacity, due to the layered manner the computational complexity is added, especially when compared with its precursor. Attributable to this compositional approach they are especially hard-wired to learn a nested hierarchy of concepts [27]. As an approach to soft-computing, Neural Networks stand in opposition to the precisely stated view of analytical algorithms that, unlike the human mind, are not tolerant of imprecision, uncertainty, partial truth and approximation [5]. In conjunction with other Deep Learning models, they stand at the vanguard of Artificial Intelligence Research, employed in tasks that previously have been found computationally intractable.