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Training Deep Neural Networks with Low Precision Input Data: A Hurricane Prediction Case Study

机译:用低精度输入数据训练深度神经网络:飓风预测案例研究

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Training deep neural networks requires huge amounts of data. The next generation of intelligent systems will generate and utilise massive amounts of data which will be transferred along machine learning workflows. We study the effect of reducing the precision of this data at early stages of the workflow (i.e. input) on both prediction accuracy and learning behaviour of deep neural networks. We show that high precision data can be transformed to low precision before feeding it to a neural network model with insignificant depreciation in accuracy. As such, a high precision representation of input data is not entirely necessary for some applications. The findings of this study pave way for the application of deep learning in areas where acquiring high precision data is difficult due to both memory and computational power constraints. We further use a hurricane prediction case study where we predict the monthly number of hurricanes on the Atlantic Ocean using deep neural networks. We train a deep neural network model that predicts the number of hurricanes, first, by using high precision input data and then by using low precision data. This leads to only a drop in prediction accuracy of less than 2%.
机译:训练深度神经网络需要大量数据。下一代智能系统将生成并利用大量数据,这些数据将沿着机器学习工作流程进行传输。我们研究了在工作流程的早期阶段(即输入)降低此数据的精度对深度神经网络的预测准确性和学习行为的影响。我们表明,在将数据馈送到精度不明显下降的神经网络模型之前,可以将高精度数据转换为低精度。这样,对于某些应用,输入数据的高精度表示并不是完全必要的。这项研究的发现为在由于内存和计算能力限制而难以获取高精度数据的领域中深度学习的应用铺平了道路。我们进一步使用飓风预测案例研究,使用深度神经网络预测大西洋上飓风的每月数量。我们训练一个深度神经网络模型,该模型首先通过使用高精度输入数据,然后通过使用低精度数据来预测飓风的数量。这只会导致预测精度下降不到2%。

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