自然语言工程(Natural Language Engineering)满足了自动语言处理各个领域的专业人员和研究人员的需求,无论是从理论还是语料库语言学、翻译、词典编纂、计算机科学还是工程学的角度。其目的是在传统的计算语言学研究和实际应用之间架起一座桥梁。除了出版关于广泛主题的原创研究文章——从文本分析、机器翻译、信息检索、语音处理和生成到集成系统和多模态接口——它还出版关于特定自然语言处理方法、任务或应用程序的特刊。 官网地址:http://dblp.uni-trier.de/db/journals/nle/

【olive: Professional open-source NLE video editor】http://t.cn/EMt1B8u Olive是一款适用于Windows,macOS和Linux的开源的专业非线性视频编辑器。能开源,并且跨三大操作系统平台的视频编辑器不多,还不get√走起?下载地址: http://t.cn/EMcPDe6 源代码GitHub地址:http://t.cn/EMt1B8u

0+
0+

Weather forecasting is usually solved through numerical weather prediction (NWP), which can sometimes lead to unsatisfactory performance due to inappropriate setting of the initial states. In this paper, we design a data-driven method augmented by an effective information fusion mechanism to learn from historical data that incorporates prior knowledge from NWP. We cast the weather forecasting problem as an end-to-end deep learning problem and solve it by proposing a novel negative log-likelihood error (NLE) loss function. A notable advantage of our proposed method is that it simultaneously implements single-value forecasting and uncertainty quantification, which we refer to as deep uncertainty quantification (DUQ). Efficient deep ensemble strategies are also explored to further improve performance. This new approach was evaluated on a public dataset collected from weather stations in Beijing, China. Experimental results demonstrate that the proposed NLE loss significantly improves generalization compared to mean squared error (MSE) loss and mean absolute error (MAE) loss. Compared with NWP, this approach significantly improves accuracy by 47.76%, which is a state-of-the-art result on this benchmark dataset. The preliminary version of the proposed method won 2nd place in an online competition for daily weather forecasting.

0+
0+
下载
预览
父主题
Top