Factorized Random Synthesized Attention
Factorized Random Synthesized Attention
**Factorized Random Synthesized Attention**, introduced with the [Synthesizer](https://paperswithcode.com/method/synthesizer) architecture, is similar to [factorized dense synthesized attention](https://paperswithcode.com/method/factorized-dense-synthesized-attention) but for random synthesizers. Letting $R$ being a randomly initialized matrix, we factorize $R$ into low rank matrices $R\_{1}, R\_{2} \in \mathbb{R}^{l\text{ x}k}$ in the attention function:

$$ Y = \text{Softmax}\left(R\_{1}R\_{2}^{T}\right)G\left(X\right) . $$

Here $G\left(.\right)$ is a parameterized function that is equivalent to $V$ in [Scaled Dot-Product Attention](https://paperswithcode.com/method/scaled).

For each head, the factorization reduces the parameter costs from $l^{2}$ to $2\left(lk\right)$ where
$k << l$ and hence helps prevent overfitting. In practice, we use a small value of $k = 8$.

The basic idea of a  Random Synthesizer is to not rely on pairwise token interactions or any information from individual token but rather to learn a task-specific alignment that works well globally across many samples.
Synthesizer: Rethinking Self-Attention in Transformer Models
https://paperswithcode.com/paper/synthesizer-rethinking-self-attention-in
Synthesizer: Rethinking Self-Attention in Transformer Models