This model archictecture has multiple attention layers.Deep-Q modelsSSL modelsTransformers

This model archictecture has multiple attention layers.

How does an attention model differ from a traditional model?The traditional model uses the input embedding directly in the decoder to get more context.The decoder does not use any additional information.The decoder only uses the final hidden state from the encoder.Attention models pass a lot more information to the decoder.

Which type of models are designed to handle large-scale image recognition tasks?Group of answer choicesRecurrent Neural NetworksGated Recurrent UnitsLarge Language ModelsLarge Vision Models

The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an 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. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.

How does an attention model differ from a traditional model?The decoder only uses the final hidden state from the encoder.Attention models pass a lot more information to the decoder.The traditional model uses the input embedding directly in the decoder to get more context.The decoder does not use any additional information.