efficient multi-head self-attention

多头自注意力机制是一种能够同时关注输入序列中不同位置信息的注意力机制。通过利用多个注意力头，使模型能够在一次计算中同时获取多个不同的关注权重，从而更加高效地捕捉输入序列中的信息。

首先，多头自注意力机制将输入序列进行线性变换，将输入特征映射到不同的子空间上。然后，在每个子空间上应用独立的注意力头，分别计算不同位置的注意力权重。这样不同的子空间可以分别捕捉输入序列中的不同语义信息，使得模型能够更加全面地理解输入序列。

通过使用多头自注意力机制，模型可以在不同的子空间上并行计算关注权重，从而提高计算的效率。同时，通过多个独立的注意力头，模型可以捕捉到更多不同位置的相关信息，提高了模型对输入序列的建模能力。

总之，efficient multi-head self-attention通过并行计算和多头独立关注不同位置信息，能够更高效地捕捉输入序列中的信息，提高了模型对输入序列的表示能力。

相关问题

Efficient Video Recommendation with Multi-Head Self-Attention and Hybrid Sampling

Efficient Video Recommendation with Multi-Head Self-Attention and Hybrid Sampling is a research paper that proposes a new approach for video recommendation. The paper presents a hybrid sampling strategy that combines the benefits of uniform and adaptive sampling techniques. It also introduces a multi-head self-attention mechanism that captures both global and local dependencies in the video sequences. The proposed method is designed to be computationally efficient and can handle large-scale video datasets.

The authors evaluated their approach on a public video recommendation dataset and demonstrated that it outperforms several state-of-the-art methods in terms of recommendation accuracy and computational efficiency. The proposed method is expected to be useful in various video recommendation applications, such as personalized video recommendations, video summarization, and video search.

Overall, Efficient Video Recommendation with Multi-Head Self-Attention and Hybrid Sampling is an interesting research paper that proposes a novel approach to video recommendation with practical applications.