Transformer

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• Sequence to Sequence (Seq2Seq)
• Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Recurrent Neural Network (RNN)
• Natural Language Processing (NLP)
• Memory Networks

Attention mechanisms in neural networks are about memory access. That’s the first thing to remember about attention: it’s something of a misnomer. A Beginner's Guide to Attention Mechanisms and Memory Networks | Skymind

Transformer Model - The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an Autoencoder (AE) / Encoder-Decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Attention Is All You Need | A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, and I. Polosukhin

Attention Is All You Need

The dominant sequence transduction models are based on complex recurrent (Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Recurrent Neural Network (RNN)) or (Deep) Convolutional Neural Network (DCNN/CNN) in an encoder-decoder (Autoencoder (AE) / Encoder-Decoder} configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Attention Is All You Need | A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, and I. Polosukhin - Google