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通过对角排序通过Seq2seq模型进行增强的矩阵推断

Wei Peng^1,2, Yisong Wang^1,2, Maonian Wu^3,4

¹Department of Computer Science, Guizhou University, Jiaxiu South Road, Guiyang, 550000, Guizhou, China.

Scientific reports

|January 10, 2024

概括

序列对序列 (seq2seq) 模型与未见等级的矩阵作斗争. 一种基于对角线的新排序方法改善了对这些矩阵到序列任务的推断,增强了seq2seq模型的功能.

科学领域:

人工智能的人工智能
机器学习机器学习
计算数学计算数学计算数学

背景情况:

序列对序列 (seq2seq) 模型在自然语言处理方面表现出色.
将seq2seq模型扩展到数学计算是有希望的,将任务视为机器翻译.
一般的排序策略限制seq2seq模型在推断看不见等级的矩阵上.

研究的目的:

为解决基于矩阵的任务seq2seq模型中通用排序的局限性.
为矩阵到序列过程提出一种基于对角线的新分类方法.
增强seq2seq模型对于不同等级的矩阵的推理能力.

主要方法:

开发了基于对角线的排序方法,以创建主要子矩阵的稳定元素排序.
为共享的领先主子矩阵部分构建稳定的排序结构.
与seq2seq模型进行比较,将通用分类与拟议的对角线分类进行比较.

主要成果:

拟议的基于对角线的分类显著改善了seq2seq模型推断在未见排名的矩阵上.
涉及最大独立集合和苏多库定律的实验验证实了该方法的有效性.
基于对角线的分类克服了通用方法的低最佳性能.

结论:

基于对角线的排序增强了seq2seq模型适用于矩阵包含法调查的适用性.
这种新方法将seq2seq模型的应用扩展到复杂的矩阵相关的计算任务.
这种方法为使用seq2seq架构的矩阵推理问题提供了更强大的解决方案.

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