Attention Is All You Need

The Transformer model relies entirely on self-attention and multi-head attention, eliminating RNNs and CNNs.

It uses an encoder-decoder structure, where each layer incorporates multi-head attention and position-wise feed-forward networks.

Positional encodings are added to input embeddings to inject sequence order information, as there are no recurrent connections.

The architecture allows for much greater parallelization during training, drastically reducing training times.

It achieves superior performance (higher BLEU scores) on machine translation tasks.

Self-attention reduces the path length for learning long-range dependencies to a constant number of operations.

Self-Attention

Self-attention allows a model to weigh the importance of all other elements in a sequence when processing a single element, capturing global context efficiently.

Scaled Dot-Product Attention

Scaled Dot-Product Attention is the efficient and numerically stable method for computing attention weights in the Transformer, crucial for its performance.

Multi-Head Attention

Multi-Head Attention allows the Transformer to capture a wider range of relationships and dependencies within the data by processing information from multiple 'perspectives' simultaneously.

Positional Encoding

Positional encodings provide essential sequence order information to the Transformer, compensating for the lack of recurrence or convolution.