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  2. Attention (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Attention_(machine_learning)

    Self-attention is essentially the same as cross-attention, except that query, key, and value vectors all come from the same model. Both encoder and decoder can use self-attention, but with subtle differences. For encoder self-attention, we can start with a simple encoder without self-attention, such as an "embedding layer", which simply ...

  3. File:Encoder self-attention, detailed diagram.png - Wikipedia

    en.wikipedia.org/wiki/File:Encoder_self...

    You are free: to share – to copy, distribute and transmit the work; to remix – to adapt the work; Under the following conditions: attribution – You must give appropriate credit, provide a link to the license, and indicate if changes were made.

  4. File:Decoder self-attention with causal masking, detailed ...

    en.wikipedia.org/wiki/File:Decoder_self...

    You are free: to share – to copy, distribute and transmit the work; to remix – to adapt the work; Under the following conditions: attribution – You must give appropriate credit, provide a link to the license, and indicate if changes were made.

  5. File:Encoder cross-attention, multiheaded version.png

    en.wikipedia.org/wiki/File:Encoder_cross...

    You are free: to share – to copy, distribute and transmit the work; to remix – to adapt the work; Under the following conditions: attribution – You must give appropriate credit, provide a link to the license, and indicate if changes were made.

  6. File:Self-attention in CNN, RNN, and self-attention.svg

    en.wikipedia.org/wiki/File:Self-attention_in_CNN...

    You are free: to share – to copy, distribute and transmit the work; to remix – to adapt the work; Under the following conditions: attribution – You must give appropriate credit, provide a link to the license, and indicate if changes were made.

  7. Transformer (deep learning architecture) - Wikipedia

    en.wikipedia.org/wiki/Transformer_(deep_learning...

    Each encoder layer consists of two major components: a self-attention mechanism and a feed-forward layer. It takes an input as a sequence of input vectors, applies the self-attention mechanism, to produce an intermediate sequence of vectors, then applies the feed-forward layer for each vector individually.

  8. React Native - Wikipedia

    en.wikipedia.org/wiki/React_Native

    React Native is an open-source UI software framework developed by Meta Platforms (formerly Facebook Inc.). [3] It is used to develop applications for Android , [ 4 ] : §Chapter 1 [ 5 ] [ 6 ] Android TV , [ 7 ] iOS , [ 4 ] : §Chapter 1 [ 6 ] macOS , [ 8 ] tvOS , [ 9 ] Web , [ 10 ] Windows [ 8 ] and UWP [ 11 ] by enabling developers to use the ...

  9. File:Multiheaded attention, block diagram.png - Wikipedia

    en.wikipedia.org/wiki/File:Multiheaded_attention...

    Multiheaded_attention,_block_diagram.png (656 × 600 pixels, file size: 32 KB, MIME type: image/png) This is a file from the Wikimedia Commons . Information from its description page there is shown below.