神经架构选择作为纳什平衡与批处理纠
概括
本研究介绍了一个架构游戏,用于在可微分神经架构搜索 (DARTS) 中对神经架构进行分辨. 这种新的方法有效地识别出最佳的单路径架构,优于现有的方法.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算机科学 计算机科学
背景情况:
- 微分神经架构搜索 (DARTS) 方法在分辨一拍架构方面面临着挑战.
- 现有的启发式或渐进式搜索方法效率低下,容易产生局部最佳.
研究的目的:
- 开发一种新且高效的方法,从一次性模型中对单路径架构进行分辨和选择.
- 为了解决当前DARTS离散技术的局限性.
主要方法:
- 以"保持"和"放弃"策略的"架构游戏"来制定架构搜索.
- 识别游戏的纳什平衡以提取最佳的单路径架构.
- 采用迷你批量的纠高斯表示,灵感来自帕伦多悖论,以提高效率.
主要成果:
- 与最先进的技术相比,拟议的方法显著加快了分密化过程.
- 这种方法以更高的最大精度实现了竞争性性能.
- 通过对基准数据集进行广泛的实验来证明有效性.
结论:
- 架构游戏框架为DARTS离散提供了有效和高效的解决方案.
- 这种新方法克服了以前方法的局限性,提供了更好的速度和准确性.
- 这项工作通过完善架构搜索策略来推进自动机器学习领域.
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