Model Architecture

A Model Architecture is a system architecture that defines the structural design and component organization of a machine learning model.

• AKA: Network Architecture, Model Structure, Model Design.
• Context:
  • It can typically determine Model Capacity through architectural parameter counts.
  • It can typically influence Model Performance via architectural inductive biases.
  • It can often enable Model Function through architectural component arrangements.
  • It can often constrain Model Training via architectural gradient flows.
  • It can range from being a Shallow Model Architecture to being a Deep Model Architecture, depending on its architectural layer depth.
  • It can range from being a Feed-Forward Model Architecture to being a Recurrent Model Architecture, depending on its architectural connection pattern.
  • It can range from being a Single-Branch Model Architecture to being a Multi-Branch Model Architecture, depending on its architectural pathway count.
  • It can range from being a Dense Model Architecture to being a Sparse Model Architecture, depending on its architectural connectivity degree.
  • ...
• Examples:
  • Neural Network Architectures, such as:
    • Convolutional Neural Network Architecture for spatial feature extraction.
    • Transformer Architecture for sequence modeling.
    • Graph Neural Network Architecture for relational data processing.
  • Ensemble Architectures, such as:
    • Random Forest Architecture for decision aggregation.
    • Boosting Architecture for sequential improvement.
    • Mixture of Experts Architecture for specialized processing.
  • Hybrid Architectures, such as:
    • CNN-RNN Architecture for video processing.
    • Transformer-CNN Architecture for vision-language tasks.
    • GNN-Transformer Architecture for graph-sequence modeling.
  • Component Architectures, such as:
    • Attention Architecture for relevance weighting.
    • Encoder-Decoder Architecture for sequence transformation.
    • Shared Encoder Architecture for multi-task learning.
  • ...
• Counter-Examples:
  • Training Algorithm, which defines how to optimize rather than structure.
  • Loss Function, which measures performance rather than defines structure.
  • Dataset, which provides data rather than model organization.
• See: System Architecture, Neural Network, Deep Learning System, Model Design, Network Topology, Architectural Pattern, Model Component.

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