Robust channel coding strategies for machine learning data
Robust channel coding strategies for machine learning data
复制标题
机器学习数据的鲁棒通道编码策略
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
L. Dolecek
中科院分区：
文献类型：
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作者：
Kayvon Mazooji;Frederic Sala;Guy Van den Broeck;L. Dolecek
最近的两个重要趋势是学习算法的扩散以及存储在不可靠的存储介质上的数据的大量增加。这些趋势相互影响；嘈杂的数据可能会对学习算法提供的结果产生不良影响。尽管存在传统的工具来提高数据存储设备的可靠性，但这些工具在不同的抽象级别运行，因此忽略了数据应用程序，从而导致资源使用效率低下。在本文中，我们建议在决定如何最好地保护数据时考虑学习算法的操作。具体而言，我们检查了几种基于存储在嘈杂介质上的数据的学习算法，并受到冗余预算有限的错误校正代码的保护；我们开发了一种分配资源的原则方法，以便将学习算法的输出的损害最小化。
Two important recent trends are the proliferation of learning algorithms along with the massive increase of data stored on unreliable storage mediums. These trends impact each other; noisy data can have an undesirable effect on the results provided by learning algorithms. Although traditional tools exist to improve the reliability of data storage devices, these tools operate at a different abstraction level and therefore ignore the data application, leading to an inefficient use of resources. In this paper we propose taking the operation of learning algorithms into account when deciding how to best protect data. Specifically, we examine several learning algorithms that operate on data that is stored on noisy mediums and protected by error-correcting codes with a limited budget of redundancy; we develop a principled way to allocate resources so that the harm on the output of the learning algorithm is minimized.