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  • Sinusoidal embedding - Attention is all you need
    In Attention Is All You Need, the authors implement a positional embedding (which adds information about where a word is in a sequence) For this, they use a sinusoidal embedding: PE(pos,2i) = si
  • What exactly are keys, queries, and values in attention mechanisms?
    The following is based solely on my intuitive understanding of the paper 'Attention is all you need' Say you have a sentence: I like Natural Language Processing , a lot ! Assume that we already have input word vectors for all the 9 tokens in the previous sentence So, 9 input word vectors Looking at the encoder from the paper 'Attention is
  • Why use multi-headed attention in Transformers? - Stack Overflow
    Attention is All You Need (2017) As such, multiple attention heads in a single layer in a transformer is analogous to multiple kernels in a single layer in a CNN : they have the same architecture, and operate on the same feature-space, but since they are separate 'copies' with different sets of weights, they are hence 'free' to learn different
  • Attention is all you need, keeping only the encoding part for video . . .
    I am trying to modify a code that could find in the following link in such a way that the proposed Transformer model that is related to the paper: all you need is attention would keep only the Encoder part of the whole Transformer model
  • Attention is all you need: from where does it get the encoder decoder . . .
    In "Attention is all you need" paper, regarding encoder (and decoder) input embeddings: Do they use already pretrained such as off the shelf Word2vec or Glove embeddings ? or are they also trained starting from random initialization One Hot Encoding ?
  • Adam optimizer with warmup on PyTorch - Stack Overflow
    In the paper Attention is all you need, under section 5 3, the authors suggested to increase the learning rate linearly and then decrease proportionally to the inverse square root of steps How do we implement this in PyTorch with Adam optimizer? Preferably without additional packages
  • Computational Complexity of Self-Attention in the Transformer Model
    So, the main idea of the Attention is all you need paper was to replace the RNN layers completely with attention mechanism in seq2seq setting because RNNs were really slow to train If you look at the Table 1 in this context, you see that it compares RNN, CNN and Attention and highlights the motivation for the paper: using Attention should have
  • What is masking in the attention if all you need paper?
    Why does it need positional encoding? What is Masked Multi-head attention? Why isn't the output of the encoder connected to the input of the decoder? Instead, it is connected to the multi-head attention? Two arrows represent the output of the encoder; what does it describe? I need a layman's understanding of the above questions
  • transformers - Attention is all you need: During training, does decoder . . .
    This is called non-autoregressive machine translation, but in this case, you need to deal with the fact output tokens become conditionally independent This can be solved e g using the CTC loss or by iterative masking and improving the output
  • Attention dropout, where was it proposed used first?
    Attention dropout (dropout on the attention weights) is very common for the Transformer model In the original Attention is all you need paper, dropout is mentioned, but not for the attention weights However, it is already part in the initial public push of Tensor2Tensor





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