Attention in vanilla Transformers
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Multi-head self-attention: multiple attention projections are computed and then concatenated into a single representation
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Masked attention: self-attention modules in the decoder are adapted to prevent each position from attending to subsequent position
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Cross-attention: in the decoder, the queries are projected from the outputs of the previous (decoder) layer, whereas the keys and values are projected using the outputs of the encoder
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Data Science
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Foundational Deep Learning Architectures: Transformers and Residual Networks @ University of Michigan - Ann Arbor
Ch.1 Transformer Architecture and Components - Foundational Deep Learning Architectures: Transformers and Residual Networks @ University of Michigan - Ann Arbor
Multi-Head Attention - Foundational Deep Learning Architectures: Transformers and Residual Networks @ University of Michigan - Ann Arbor
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A research team is building a model to summarize extremely long scientific papers. They are comparing two distinct architectural approaches:
- Approach 1: Processes the input text sequentially, token by token, updating an internal state that is passed from one step to the next.
- Approach 2: Processes all input tokens simultaneously, using a mechanism that directly relates every token to every other token in the input to determine context.
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Attention in vanilla Transformers