What is Random Synthesized Attention?

Random Synthesized Attention is a type of attention used in machine learning models. It is different from other types of attention because it does not depend on the input tokens. Instead, the attention weights are initialized randomly.

This attention method was introduced with the Synthesizer architecture. Random Synthesized Attention is used to improve the performance of these models by learning a task-specific alignment that works well globally across many samples.

How Does Random Synthesized Attention Work?

Random Synthesized Attention works by using a randomly initialized matrix called R. This matrix is represented as R∈Rl×l.

Next, the attention weights are computed using the Softmax function applied to R. G(X) is the input to the attention layer, where X represents the input matrix.

Each head adds two parameters to the overall network. Random Synthesized Attention does not rely on pairwise token interactions or any information from individual tokens.

Random Synthesized Attention vs Dense Synthesized Attention

Random Synthesized Attention is different from Dense Synthesized Attention. Dense Synthesized Attention conditions on each token independently, as opposed to pairwise token interactions in the vanilla Transformer model.

Random Synthesized Attention is a direct generalization of the recently proposed fixed self-attention patterns of Raganato et al (2020).

The Importance of Random Synthesized Attention in Machine Learning

Random Synthesized Attention is an important tool for improving machine learning models. The basic idea of Random Synthesized Attention is to not rely on pairwise token interactions or any information from individual tokens.

Instead, Random Synthesized Attention learns a task-specific alignment that works well globally across many samples. This approach can be very effective when working with large datasets or complex problems.

Random Synthesized Attention is a powerful tool for improving the performance of machine learning models. By not relying on pairwise token interactions or any information from individual tokens, Random Synthesized Attention can learn a task-specific alignment that works well globally across many samples.

With the increasing availability of large datasets and the growing complexity of machine learning problems, Random Synthesized Attention is likely to become even more important in the future.

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