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Updated: May 10, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

通过表示学习解决两阶段的随机整数程序.

Yaoxin Wu¹, Zhiguang Cao², Wen Song³

¹Department of Industrial Engineering and Innovation Sciences, Eindhoven University of Technology, Eindhoven, 5600 MB, The Netherlands.

Neural networks : the official journal of the International Neural Network Society

|April 21, 2025

概括

此摘要是机器生成的。

本研究引入了一个条件变量自编码器 (CVAE),以有效地解决复杂的随机整数程序 (SIP). 该方法学习场景表示,减少计算时间并提高大规模问题的解决方案质量.

关键词:

有条件变化的自编码器.相反的学习学习.半监督学习半监督学习静态的整数程序静态的整数程序

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相关实验视频

Last Updated: May 10, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

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Published on: May 13, 2022

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Published on: July 25, 2013

科学领域:

运营研究运营研究
机器学习机器学习
计算优化计算优化

背景情况:

随机整数程序 (SIP) 由于其复杂性,在计算上具有挑战性.
现有的方法与大规模的SIP实例和多样化的场景分布作斗争.

研究的目的:

开发一种有效的方法来解决双阶段随机整数程序 (SIP).
在优化中利用机器学习进行场景表示学习.

主要方法:

使用图形卷积网络 (GCN) 的条件变化自编码器 (CVAE) 将场景嵌入到潜在空间中.
包括客观预测和场景对比在内的辅助任务用于整合客观信息.
梯度反向传播精细化了学习的表示.

主要成果:

学习场景表示显著减少了SIP所需的场景数量.
该方法在缩短的计算时间内实现高质量的解决方案.
该方法在更大的实例和各种数据分布上表现出有效性.

结论:

拟议的基于CVAE的场景表示学习对解决SIP有效.
这种方法为复杂的优化问题提供了计算效率高且可扩展的解决方案.
该技术在不同的问题大小和数据特征中很好地泛化.