Recurrent neural nets had disadvantages that limited ... it is fed into the transformer’s encoder module. Unlike RNN and LSTM models, the transformer does not receive one input at a time.

Transformers are very powerful, and also very complex. They use a dense feedforward network as a sub-neural net inside the encoder and decoder components. They also demand considerable computing ...

These recurrent neural networks processed text ... Transformer architecture to create BERT (Bidirectional Encoder Representations from Transformers). BERT drastically improved the way machines ...

The first of these technologies is a translation model architecture — a hybrid architecture consisting of a Transformer encoder and a recurrent neural network (RNN) decoder implemented in Lingvo ...

We demonstrate a path to software-equivalent accuracy for the GLUE benchmark on BERT (Bidirectional Encoder Representations from ... Toward Software-Equivalent Accuracy on Transformer-Based Deep ...