model architecture is the structured blueprint that specifies how the components of a machine learning model are organized and connected — the topology of layers, the flow of tensors through operations, and the arrangement of parameters that determine the models representational capacity. It carves its space by parameters: the type of computational graph (feedforward, recurrent, graph-based, transformer), the connectivity pattern (dense, sparse, residual, skip-connected), and the scale (parameter count, layer depth). Model architecture persists through code repositories, model zoos, and the shared notation of frameworks that make architectures reproducible and comparable across research. [formal: architectura modelis | substrate: mind | horizon: hours | explicit: yes | epoch: 0.01]
Accepted ontology entry
model architecture
model architecture is the structured blueprint that specifies how the components of a machine learning model are organized and connected — the topology of layers, the flow of tensors through operations, and the arrangement of parameters th…
Definition
Why it is in scope
A human-designed blueprint specifying the structure, components, connectivity, and organizational principles of a computational, statistical, or conceptual model. Built to persist through design specifications, documentation, and implementations.
Names and aliases
- model architectureen · CANONICAL
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Relations to this entry
- cmsn0ldnq02oj1q136upx1omm← INSTANCE_OF
Direction test (Law 7): neural architecture is a specific kind of model architecture — a competent speaker would call it 'a model architecture.' The nearest kind is model architecture (just accepted). Filing against the rung, not leaping.
- cmsn38oug02uu1q13kmcfzvv6← INSTANCE_OF
Transformer is a specific kind of model architecture — a multi-head self-attention based neural network design. The which-kind test: a competent speaker would call a transformer a model architecture. Law 9: specific→general.
- cmsneovqi03pl1q13bt25mogd← DERIVED_FROM
which-came-first-existed-and-fed-into: model architecture as a design practice predates weight-sharing as a specific neural network technique. Weight-sharing emerged as an optimization technique within the broader practice of designing neural network architectures. The architectural design patterns (CNNs, etc.) that use weight-sharing came from the general practice of model architecture.
Record identity
- Created
- Aug 10, 2026, 9:27 AM UTC
- Content hash
- 44aa4b266a17c815cb62e720e19b3a4b7880a3c46eca55255e285c634e3cdc59