Results 31 to 40 of about 13,581,527 (336)
Nucleic Acids Res., 2018 Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein–protein associations is
Damian Szklarczyk +11 more
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Overcoming catastrophic forgetting in neural networks [PDF]
Proceedings of the National Academy of Sciences of the United States of America, 2016 Significance Deep neural networks are currently the most successful machine-learning technique for solving a variety of tasks, including language translation, image classification, and image generation. One weakness of such models is that, unlike humans,
J. Kirkpatrick +13 more
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node2vec: Scalable Feature Learning for Networks [PDF]
Knowledge Discovery and Data Mining, 2016 Prediction tasks over nodes and edges in networks require careful effort in engineering features used by learning algorithms. Recent research in the broader field of representation learning has led to significant progress in automating prediction by ...
Aditya Grover, J. Leskovec
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基于精定位余震序列的2019年四川长宁MS6.0地震等震线研究
Dizhen xuebao, 2020 采用双差定位法对四川长宁MS6.0主震和24小时内的余震进行重新定位，对重定位的余震进行震级加权的方向分布拟合得到余震区的长轴和中心点，并与Ⅶ度烈度等震线长轴和等震线几何中心进行比较，结果显示：余震区长轴方向与Ⅶ度区长轴方向相近（差值为4°），且长度近似相等（差值为4 km）；余震中心与等震线中心均位于重定位主震的西北方向，且等震线中心相对余震中心向上盘方向偏移约3.0 km。长宁MS6.0地震等震线图呈现出明显的上/下盘效应，上盘高烈度区面积约为下盘的2.1倍，等震线中心也向上盘方向偏移 ...
Zhishuang Xu +5 more
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British Machine Vision Conference, 2016 Deep residual networks were shown to be able to scale up to thousands of layers and still have improving performance. However, each fraction of a percent of improved accuracy costs nearly doubling the number of layers, and so training very deep residual ...
Sergey Zagoruyko, N. Komodakis
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Image-to-Image Translation with Conditional Adversarial Networks [PDF]
Computer Vision and Pattern Recognition, 2016 We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping.
Phillip Isola +3 more
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The Journal of Engineering, 2019 In 2017, 34.8% of total electricity consumption in Northern Ireland was generated from renewable sources located in Northern Ireland. Alongside other significant changes to the electricity sector this leads to an increasing need for reactive power.
Jonathan Pollock +3 more
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Influence of the tie-break rule on the end-vertex problem [PDF]
Discrete Mathematics & Theoretical Computer Science, 2014 End-vertices of a given graph search may have some nice properties, as for example it is well known that the last vertex of Lexicographic Breadth First Search (LBFS) in a chordal graph is simplicial, see Rose, Tarjan and Lueker 1976.
Pierre Charbit +2 more
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Towards Evaluating the Robustness of Neural Networks [PDF]
IEEE Symposium on Security and Privacy, 2016 Neural networks provide state-of-the-art results for most machine learning tasks. Unfortunately, neural networks are vulnerable to adversarial examples: given an input x and any target classification t, it is possible to find a new input x' that is ...
Nicholas Carlini, D. Wagner
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地震科学进展, 2022 In this paper, the regional full waveform inversion of the broadband waveforms from China Seismic Network were conducted, and the focal mechanism solutions of the 18 earthquakes with M≥4.0 in the mainland of China from July 1 to September 30, 2022 were ...
Shanshan Liang +4 more
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