Transformer

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• Attention
• Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Recurrent Neural Network (RNN)
• Bidirectional Encoder Representations from Transformers (BERT)
• Natural Language Processing (NLP)
• Memory Networks
• Transformer-XL
• Tensor2Tensor (T2T) | Google Brain
  • Tensor2Tensor Transformers: New Deep Models for NLP | Łukasz Kaiser
  • Tensor2Tensor | GitHub
  • Tensor2Tensor Library | GitHub
• The Illustrated Transformer | Jay Alammar
• Sequence to Sequence (Seq2Seq)

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