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BatTS:一种用于优化深度前神经网络的混合方法
Sichen Pan1, Tarun Kumar Gupta2, Khalid Raza2
1School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, Guangdong Province, China.
PeerJ. Computer science
|June 22, 2023
概括
设计深度前神经网络 (DFNN) 架构具有挑战性. 一种新的混合 BatTS 方法使用 Bat 算法和 Tabu 搜索优化了 DFNN 架构,提高了随机试验的性能.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 深度前神经网络 (DFNNs) 在计算任务中取得了显著的成功.
- 目前的DFNN架构选择依赖于低效的手动或试错方法.
- 自动化DFNN架构设计对于实现最先进的性能至关重要,但仍然是繁的.
研究的目的:
- 引入一种新的混合方法,BATTS,用于优化深度前神经网络架构.
- 为了提高DFNN架构设计流程的效率和有效性.
- 通过自动化架构优化来提高DFNNs的整体性能.
主要方法:
- 一种混合方法 (BatTS) 整合了蝙蝠算法,塔布搜索 (TS) 和渐变下降与动量 (GDM) 逆向传播.
- 由蝙蝠算法指导的动态架构生成.
- 高效的架构评估和局部最佳逃脱,由 Tabu 搜索提供便利.
- 在四个不同的基准数据集上进行实证评估.
主要成果:
- 与传统的 Tabu 搜索和随机试验方法相比,BatTS 显示出更好的性能.
- 混合方法在探索和评估新架构方面表现出更高的效率.
- BatTS的动态性质有助于发现优越的DFNN架构.
- 在多个基准数据集中观察到一致的性能增长.
结论:
- 拟议的BatTS混合方法为优化DFNN架构提供了更有效和高效的方法.
- 通过将元启发式优化与强有力的训练相结合,BatTS克服了手动设计和随机搜索的局限性.
- 这项工作为寻求通过智能架构设计推进DFNN性能的研究人员和从业人员提供了有价值的工具.
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