| Popis: |
The study of abstraction and composition - the focus of category theory - naturally leads to sophisticated diagrams which can encode complex algebraic semantics. Consequently, these diagrams facilitate a clearer visual comprehension of diverse theoretical and applied systems. Complex algebraic structures - otherwise represented by a forest of symbols - can be encoded into diagrams with intuitive graphical rules. The prevailing paradigm for diagrammatic category theory are monoidal string diagrams whose specification reflects the axioms of monoidal categories. However, such diagrams struggle in accurately portraying crucial categorical constructs such as functors or natural transformations, obscuring central concepts such as the Yoneda lemma or the simultaneous consideration of hom-functors and products. In this work, we introduce functor string diagrams, a systematic approach for the development of categorical diagrams which allows functors, natural transformations, and products to be clearly represented. We validate their practicality in multiple dimensions. We show their usefulness for theoretical manipulations by proving the Yoneda lemma, show that they encompass monoidal string diagrams and hence their helpful properties, and end by showing their exceptional applied utility by leveraging them to underpin neural circuit diagrams, a method which, at last, allows deep learning architectures to be comprehensively and rigorously expressed. |
