Switch Transformer architecture

Target entity: Switch Transformer architecture
Context triple: [Noam Shazeer, developed, Switch Transformer architecture]

• A. Reformer architecture
  The Reformer architecture is a neural network model that improves Transformer efficiency by using locality-sensitive hashing attention and reversible layers to greatly reduce memory and computational costs.
• B. Sparse Transformer
  Sparse Transformer is a neural network architecture that uses sparse attention patterns to efficiently model long-range dependencies in sequences while reducing computational cost compared to standard Transformers.
• C. Swin Transformer
  Swin Transformer is a hierarchical vision transformer architecture that uses shifted windows for efficient and scalable image recognition and related computer vision tasks.
• D. Transformer-XL
  Transformer-XL is a neural network architecture for language modeling that extends the Transformer with segment-level recurrence and relative positional encodings to better capture long-range dependencies.
• E. Transformer
  Transformer is a neural network architecture based on self-attention mechanisms that has become the foundation for modern large language models and many state-of-the-art systems in natural language processing.
• F. None of above. chosen
• G. Unsure - the case is ambiguous/there is not enough information to decide.

Target entity: Switch Transformer architecture
Target entity description: The Switch Transformer architecture is a sparse, mixture-of-experts neural network design that routes tokens to different expert subnetworks to greatly increase model capacity while keeping computation per token relatively low.

• A. Reformer architecture
  The Reformer architecture is a neural network model that improves Transformer efficiency by using locality-sensitive hashing attention and reversible layers to greatly reduce memory and computational costs.
• B. Sparse Transformer
  Sparse Transformer is a neural network architecture that uses sparse attention patterns to efficiently model long-range dependencies in sequences while reducing computational cost compared to standard Transformers.
• C. Swin Transformer
  Swin Transformer is a hierarchical vision transformer architecture that uses shifted windows for efficient and scalable image recognition and related computer vision tasks.
• D. Transformer-XL
  Transformer-XL is a neural network architecture for language modeling that extends the Transformer with segment-level recurrence and relative positional encodings to better capture long-range dependencies.
• E. Transformer
  Transformer is a neural network architecture based on self-attention mechanisms that has become the foundation for modern large language models and many state-of-the-art systems in natural language processing.
• F. None of above. chosen