annN

AnnN is a term encountered in some machine learning and artificial intelligence discussions to describe a class of artificial neural networks defined by a modular architecture consisting of N distinct processing units or subnetworks. The exact meaning of N varies by source, and there is no universally standardized definition. In general, annN emphasizes decomposing a problem into multiple modules that can be composed in various topologies to perform computation.

Architectural patterns commonly associated with annN include sequential stacks of modules, parallel branches that may be later fused, and hierarchical graphs where information flows through intermediate routing gates. The design goal is to enable specialization among modules, improve scalability, and facilitate reuse of components across tasks. Some variants use dynamic routing, allowing the model to select which modules participate in processing a given input.

Training and optimization for annN typically involve end-to-end gradient-based methods, though blockwise or staged training can be applied to encourage specialization. Loss functions may combine a primary objective with auxiliary losses that promote module diversity or alignment between representations. Regularization and architectural search are common to determine the optimal value of N and the module interconnections for a given problem.

AnnN concepts are related to, and sometimes overlap with, modular neural networks, mixture-of-experts, and certain forms of residual or graph-based networks. In practice, annN remains a descriptive label rather than a single standardized model, used primarily in research contexts to discuss modularity and scalability in neural architectures.

See also: artificial neural networks, modular neural networks, mixture of experts, neural architecture search. References to annN appear in a variety of experimental papers and project reports rather than as a unified standard.