Neural Architecture

Neural architecture is the structural design of artificial neural networks — the arrangement of layers, the topology of connections, the choice of activation functions, and the mechanisms by which information flows and is transformed. It is the counterpart to biological neural anatomy in the artificial domain, and it matters for the same reason biological anatomy matters: structure constrains function. A transformer cannot do what a recurrent network does, not because the training data differs but because the architecture embodies different inductive biases about how information should be processed.

The field has shifted from hand-designed architectures (convolutional networks for vision, recurrent networks for sequences) to automated architecture search (NAS) and, more recently, to the discovery that scale and data can override architectural differences — the