Transformer-based Neural Network Architecture

A Transformer-based Neural Network Architecture is a feedforward deep sequential data neural network architecture based on Transformer blocks.

• AKA: X-Former Architecture.
• Context:
  • It can (typically) consist of Transformer Encoder Layers (which perform self-attention and position-wise feed-forward operations) and Transformer Decoder Layers (which perform self-attention, encoder-decoder attention, and position-wise feed-forward operations).
  • It can (typically) be referenced by a Transformer-Based Neural Network Instance.
  • It can be a Sequential Data Model Architecture capable of capturing long-range dependencies within the data.
  • It can have a specific arrangement of Neural Network Input Layers, Neural Network Hidden Layers, and Neural Network Output Layers tailored for transformer operations.
  • It can (typically) include Self-Attention Mechanisms.
  • ...
• Example(s):
  • The original Transformer architecture, as proposed in Vaswani et al. (2017).
  • The GPT (Generative Pre-trained Transformer) Architecture, as proposed in Radford et al., 2018.
  • The Bidirectional Encoder Representations from Transformers (BERT) Architecture, introduced by Devlin et al., 2018.
  • TransformerXL Architecture.
  • T5 (Text-To-Text Transfer Transformer) Architecture.
  • A Switch Network Architecture.
  • ...
• Counter-Example(s):
  • A Convolutional Neural Network (CNN) Architecture.
  • A Traditional Feedforward Neural Network Architecture without transformer mechanisms.
  • A Recurrent Neural Network Architecture.
• See: Self-Attention, Encoder-Decoder Architecture, Language Model, Transformer-Based Neural Network, Neural Transformer, Attention Mechanism, Deep Learning.

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References

2023

• Chat
  • A Transformer Model Architecture, on the other hand, is a blueprint or template for building Transformer-based neural networks. It defines the overall structure and components of the network, including the arrangement of transformer blocks, self-attention mechanisms, feed-forward layers, and other architectural details. The architecture serves as a foundation for creating specific neural network models with different configurations, hyperparameters, and training data.

    Example: The GPT (Generative Pre-trained Transformer) architecture is a Transformer Model Architecture. It consists of a decoder-only structure composed of a stack of transformer blocks. The architecture can be used to create various Transformer-based neural networks for different tasks, such as language modeling and text generation. GPT-3 is one of the models based on the GPT architecture, and the "Davinci" model is a specific instance within the GPT-3 family.

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