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  2. Attention (machine learning) - Wikipedia

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

    An image captioning model was proposed in 2015, citing inspiration from the seq2seq model. [ 25 ] that would encode an input image into a fixed-length vector. Xu et al (2015), [ 26 ] citing Bahdanau et al (2014), [ 27 ] applied the attention mechanism as used in the seq2seq model to image captioning.

  3. 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.

  4. Transformer (deep learning architecture) - Wikipedia

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

    In addition, the scope of attention, or the range of token relationships captured by each attention head, can expand as tokens pass through successive layers. This allows the model to capture more complex and long-range dependencies in deeper layers. Many transformer attention heads encode relevance relations that are meaningful to humans.

  5. Attention Is All You Need - Wikipedia

    en.wikipedia.org/wiki/Attention_Is_All_You_Need

    Image and video generators like DALL-E (2021), Stable Diffusion 3 (2024), [44] and Sora (2024), use Transformers to analyse input data (like text prompts) by breaking it down into "tokens" and then calculating the relevance between each token using self-attention, which helps the model understand the context and relationships within the data.

  6. Vision transformer - Wikipedia

    en.wikipedia.org/wiki/Vision_transformer

    A time attention layer is where the requirement is ′ =, ′ = instead. The TimeSformer also considered other attention layer designs, such as the "height attention layer" where the requirement is ′ =, ′ =. However, they found empirically that the best design interleaves one space attention layer and one time attention layer.

  7. Recurrent neural network - Wikipedia

    en.wikipedia.org/wiki/Recurrent_neural_network

    An Elman network is a three-layer network (arranged horizontally as x, y, and z in the illustration) with the addition of a set of context units (u in the illustration). The middle (hidden) layer is connected to these context units fixed with a weight of one. [51] At each time step, the input is fed forward and a learning rule is applied. The ...

  8. Multilayer perceptron - Wikipedia

    en.wikipedia.org/wiki/Multilayer_perceptron

    In 1943, Warren McCulloch and Walter Pitts proposed the binary artificial neuron as a logical model of biological neural networks. [11]In 1958, Frank Rosenblatt proposed the multilayered perceptron model, consisting of an input layer, a hidden layer with randomized weights that did not learn, and an output layer with learnable connections.

  9. Active shutter 3D system - Wikipedia

    en.wikipedia.org/wiki/Active_shutter_3D_system

    An active shutter 3D system (a.k.a. alternate frame sequencing, alternate image, AI, alternating field, field sequential or eclipse method) is a technique for displaying stereoscopic 3D images. It works by only presenting the image intended for the left eye while blocking the right eye's view, then presenting the right-eye image while blocking ...