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使用内核函数对注意力机制的一种新的方法: Kerformer

Yao Gan¹, Yanyun Fu², Deyong Wang³

¹Information Science and Engineering Department, Xinjiang University, Ürümqi, China.

Frontiers in neurorobotics

|September 11, 2023

概括

Kerformer是一种新的AI模型,通过将注意力复杂性从二进制到线性来提高自然语言处理 (NLP) 和视觉任务. 这种人工智能进步提高了效率和准确性,特别是在长序列中.

科学领域:

人工智能的人工智能
自然语言处理自然语言处理.
计算机视觉计算机视觉

背景情况:

在人工智能模型中,传统的注意力机制对序列长度 (N) 有二次成本 (O(N^2)).
这种复杂性限制了涉及延长序列的任务的效率和可扩展性.
Kerformer是一个使用内核方法的线性变压器模型.

研究的目的:

介绍Kerformer,一种旨在克服传统注意力机制计算局限性的AI模型.
提高自然语言处理 (NLP) 和视觉任务的效率和准确性,特别是那些具有长序列的任务.

主要方法:

Kerformer采用非线性重量机制,将最大的注意力转换为基于特征的点点产品注意力.
它利用软max计算的非负性和非线性权重特征用于Query (Q) 和Key (K) 计算.
该模型包含一个SE区块,以进一步提高性能.

主要成果:

注意矩阵的时间复杂性从O(N^2) 减少到O(N).
与传统方法相比,Kerformer表现出优越的时间和内存效率.
在NLP和视觉任务中获得更高的平均准确度 (83.39%).
在长序列任务中达到58.94%的平均精度,在视觉任务中提高了效率和融合速度.

结论:

Kerformer提供了一个可扩展和高效的解决方案,用于处理AI任务中的长序列.
该模型显著降低了计算成本,同时保持或提高了准确性.
它为NLP和计算机视觉应用程序提供了有希望的进步,这些应用程序面临着序列长度限制.

关键词:

区块 SE 区块 SE 区块核心方法的核心方法.线性注意力是一种线性注意力.自己注意力自我注意力变压器变压器变压器变压器

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