Self-Attention
Weighting every position by every other position in the same sequence.
Resumen
Self-attention computes, for each position, a weighted sum of all positions in the sequence, where the weights come from comparing their query and key vectors. It solves the problem that a recurrent model has to compress everything it has seen into one state before moving on. It exists as the mechanism that removed that bottleneck, and it is what makes a transformer's cost grow with the square of the sequence length.
Grafo de tecnologías
Cómo se conecta Self-Attention con el resto del stack.
Classification
- Transformer architecture Algorithms Self-Attention part of Transformer High ✓ source
Herramientas
Software que implementa o se integra con Self-Attention.