The structural design of a neural network, specifying the arrangement of layers, connection patterns, activation functions, skip connections, normalisation methods, and attention mechanisms. Key architectures include feedforward networks, CNNs, RNNs, transformers, and graph neural networks; Neural Architecture Search automates discovery of optimal configurations for accuracy, efficiency, and hardware constraints.
Semantic Classification
Content
Key Characteristics
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Defines layer connectivity patterns and information flow
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Incorporates specialized modules (attention, residual blocks)
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Balances model capacity with computational efficiency
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Supports modular design and component reusability
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Enables architecture search and optimization
Overview
Network Architecture in AI refers to the structural design of neural networks, defining the arrangement of layers, connections, and computational units. Key architectures include feedforward networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and graph neural networks (GNNs). Architecture design involves selecting layer types, activation functions, skip connections, normalization methods, and attention mechanisms. Neural Architecture Search (NAS) automates architecture discovery through evolutionary algorithms or reinforcement learning, optimizing for accuracy, efficiency, and resource constraints.
Related Concepts
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References
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He, K. et al. (2016). Deep Residual Learning for Image Recognition. CVPR 2016.
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Zoph, B. & Le, Q. (2017). Neural Architecture Search with Reinforcement Learning. ICLR 2017.
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Tan, M. & Le, Q. (2019). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. ICML 2019.