Transformer Attention Mechanism
(Redirected from Scaled Dot-Product Attention)
Jump to navigation
Jump to search
A Transformer Attention Mechanism is an attention neural network sequence processing mechanism that computes weighted relationships between sequence elements to enable transformer attention context modeling (in transformer architectures).
- AKA: Self-Attention Mechanism, Scaled Dot-Product Attention, Multi-Head Attention Mechanism, Transformer Attention.
- Context:
- It can typically compute Transformer Attention Scores through transformer attention query-key interactions.
- It can typically generate Transformer Attention Weights via transformer attention softmax normalization.
- It can typically produce Transformer Attention Outputs using transformer attention value aggregation.
- It can typically enable Transformer Attention Parallelization for transformer attention efficient computation.
- It can typically support Transformer Attention Long-Range Dependency through transformer attention global context.
- ...
- It can often implement Transformer Attention Multi-Head Processing via transformer attention head specialization.
- It can often facilitate Transformer Attention Position Encoding through transformer attention positional information.
- It can often optimize Transformer Attention Memory Efficiency using transformer attention sparse patterns.
- It can often enable Transformer Attention Transfer Learning for transformer attention task adaptation.
- ...
- It can range from being a Simple Transformer Attention Mechanism to being a Complex Transformer Attention Mechanism, depending on its transformer attention architectural sophistication.
- It can range from being a Single-Head Transformer Attention Mechanism to being a Multi-Head Transformer Attention Mechanism, depending on its transformer attention head count.
- It can range from being a Local Transformer Attention Mechanism to being a Global Transformer Attention Mechanism, depending on its transformer attention receptive field.
- It can range from being a Dense Transformer Attention Mechanism to being a Sparse Transformer Attention Mechanism, depending on its transformer attention connectivity pattern.
- It can range from being a Bidirectional Transformer Attention Mechanism to being a Causal Transformer Attention Mechanism, depending on its transformer attention directionality.
- ...
- It can integrate with Transformer Attention Layer Normalization for transformer attention training stability.
- It can coordinate with Transformer Attention Feed-Forward Network for transformer attention feature transformation.
- It can interface with Transformer Attention Dropout Layer for transformer attention regularization.
- It can synchronize with Transformer Attention Gradient Flow for transformer attention backpropagation.
- It can combine with Transformer Attention Optimization Algorithm for transformer attention training efficiency.
- ...
- Examples:
- Standard Transformer Attention Types, such as:
- Encoder Transformer Attentions, such as:
- Decoder Transformer Attentions, such as:
- Efficient Transformer Attention Variants, such as:
- Linear Transformer Attentions, such as:
- Sparse Transformer Attentions, such as:
- ...
- Standard Transformer Attention Types, such as:
- Counter-Examples:
- Recurrent Neural Network Mechanism, which processes sequentially rather than in parallel attention computation.
- Convolutional Neural Network Mechanism, which uses local filters rather than global attention.
- Feed-Forward Network Mechanism, which lacks attention-based weighting between elements.
- See: Attention Mechanism, Transformer Architecture, Self-Attention, Multi-Head Attention, Causal Mask Mechanism, Query-Key-Value Computation, Attention Score, Softmax Function, Neural Network Mechanism, Sequence Modeling.