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题目： Boosting algorithms in energy research: A systematic review
摘要：
机器学习算法由于其灵活性、自动化和处理大数据的能力，在(可再生)能源研究中得到了广泛的应用。在最著名的机器学习算法中，有一种是增强型算法，这种算法被称为“从愚人委员会中获取智慧”，从而将学习能力差的人转化为学习能力强的人。增强算法具有较高的灵活性和可解释性。后者是统计界最近发展的结果。在这项工作中，我们提供了关于助推算法的性质的理解，以便更好地利用它们在能源研究中的优势。在这方面,(a)我们在提高算法总结最新进展,(b)我们审查有关应用在能源研究与关注可再生能源(尤其是那些专注于风能和太阳能)组成总数的很大一部分,和(c)我们描述提高算法是如何实现的以及他们如何使用它们的属性有关。我们的研究表明，到目前为止，能源领域(可再生能源在其中发挥着关键作用)的巨大进步在解释和解释方面以及在预测性能方面都是可能的。
Boosting algorithms in energy research: A systematic review
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Human pose estimation - the process of recognizing human keypoints in a given image - is one of the most important tasks in computer vision and has a wide range of applications including movement diagnostics, surveillance, or self-driving vehicle. The accuracy of human keypoint prediction is increasingly improved thanks to the burgeoning development of deep learning. Most existing methods solved human pose estimation by generating heatmaps in which the ith heatmap indicates the location confidence of the ith keypoint. In this paper, we introduce novel network structures referred to as multiresolution representation learning for human keypoint prediction. At different resolutions in the learning process, our networks branch off and use extra layers to learn heatmap generation. We firstly consider the architectures for generating the multiresolution heatmaps after obtaining the lowest-resolution feature maps. Our second approach allows learning during the process of feature extraction in which the heatmaps are generated at each resolution of the feature extractor. The first and second approaches are referred to as multi-resolution heatmap learning and multi-resolution feature map learning respectively. Our architectures are simple yet effective, achieving good performance. We conducted experiments on two common benchmarks for human pose estimation: MS-COCO and MPII dataset.
