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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: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.

  4. Attention Is All You Need - Wikipedia

    en.wikipedia.org/wiki/Attention_Is_All_You_Need

    Scaled dot-product attention & self-attention. The use of the scaled dot-product attention and self-attention mechanism instead of a Recurrent neural network or Long short-term memory (which rely on recurrence instead) allow for better performance as described in the following paragraph. The paper described the scaled-dot production as follows:

  5. Vision transformer - Wikipedia

    en.wikipedia.org/wiki/Vision_transformer

    The attention mechanism in a ViT repeatedly transforms representation vectors of image patches, incorporating more and more semantic relations between image patches in an image. This is analogous to how in natural language processing, as representation vectors flow through a transformer, they incorporate more and more semantic relations between ...

  6. Transformer (deep learning architecture) - Wikipedia

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

    For many years, sequence modelling and generation was done by using plain recurrent neural networks (RNNs). A well-cited early example was the Elman network (1990). In theory, the information from one token can propagate arbitrarily far down the sequence, but in practice the vanishing-gradient problem leaves the model's state at the end of a long sentence without precise, extractable ...

  7. Graph neural network - Wikipedia

    en.wikipedia.org/wiki/Graph_neural_network

    The graph attention network (GAT) was introduced by Petar Veličković et al. in 2018. [11] Graph attention network is a combination of a GNN and an attention layer. The implementation of attention layer in graphical neural networks helps provide attention or focus to the important information from the data instead of focusing on the whole data.

  8. PyTorch - Wikipedia

    en.wikipedia.org/wiki/PyTorch

    In September 2022, Meta announced that PyTorch would be governed by the independent PyTorch Foundation, a newly created subsidiary of the Linux Foundation. [ 24 ] PyTorch 2.0 was released on 15 March 2023, introducing TorchDynamo , a Python-level compiler that makes code run up to 2x faster, along with significant improvements in training and ...

  9. Self-attention - Wikipedia

    en.wikipedia.org/wiki/Self-attention

    Upload file; Permanent link; Page information; Cite this page; Get shortened URL; ... Self-attention can mean: Attention (machine learning), a machine learning technique;