通过正式方法压缩神经网络
Dalila Ressi1, Riccardo Romanello2, Sabina Rossi3
1Ca' Foscari University of Venice, Italy; University of Udine, Italy.
概括
这项研究介绍了一种基于马尔科夫链的可折叠性的神经网络的新型修剪方法. 这种方法可以精确地减少模型大小,而不需要重新训练或数据,解决嵌入式系统的限制.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 正式方法 正式方法
背景情况:
- 大型神经网络模型在资源有限的嵌入式设备上面临实施挑战.
- 现有的网络压缩技术通常依赖于启发式,需要重新训练,影响准确性.
- 模型缩小对于超越神经网络的验证和性能评估至关重要.
研究的目的:
- 将基于启发式的模型减少策略与马尔科夫链分析的正式概念相结合.
- 为神经网络提出一种新的修剪方法,该方法是建立在可穿性的正式概念之上的.
- 在没有数据依赖或微调的情况下,在缩小模型中实现精确的行为保存.
主要方法:
- 从马尔科夫连锁理论中利用形性的正式概念.
- 开发基于放松的穿性条件的修剪策略.
- 应用该方法来减少神经网络的复杂性.
主要成果:
- 拟议的修剪方法保留了原始模型的确切行为结果.
- 该方法消除了在模型缩小过程中对数据依赖和微调的需求.
- 通过宽松的形性定义,正式化常见的减少技术.
结论:
- 可变性为开发数据独立和不需要再培训的模型减少技术提供了正式的基础.
- 这项工作为嵌入式系统压缩神经网络提供了一个原则性的方法.
- 这些发现有助于在不同领域对模型缩小的更深入的正式理解.
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