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Development of a Prognostic Nomogram in Epithelial Ovarian Cancer Based on KELIM: A Retrospective Study at TuDu Hospital, Vietnam. [PDF]
Diagnostics (Basel)Pham HT, Vo TM, Phan LNN, Nguyen HT.
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Estabilização de solo residual de Lajeado com adição de cimento e cinza de casca de arroz
, 2017 Sara Virgínia Fritscher
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Image Super-Resolution Using Very Deep Residual Channel Attention Networks
European Conference on Computer Vision, 2018Convolutional neural network (CNN) depth is of crucial importance for image super-resolution (SR). However, we observe that deeper networks for image SR are more difficult to train.
Yulun Zhang +5 more
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Analytical Chemistry, 1965
METHODOLOGY for residue analysis has advanced rapidly during the current review period, from November 1962 through October 1964. Notable progress has been made in the development and refinement of methods of analysis by which any or all of a large number of pesticide residue chemicals can be detected and measured in one general operation.
Cook, J. William, Williams, Sidney
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METHODOLOGY for residue analysis has advanced rapidly during the current review period, from November 1962 through October 1964. Notable progress has been made in the development and refinement of methods of analysis by which any or all of a large number of pesticide residue chemicals can be detected and measured in one general operation.
Cook, J. William, Williams, Sidney
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Identity Mappings in Deep Residual Networks
European Conference on Computer Vision, 2016Deep residual networks have emerged as a family of extremely deep architectures showing compelling accuracy and nice convergence behaviors. In this paper, we analyze the propagation formulations behind the residual building blocks, which suggest that the
Kaiming He +3 more
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Image Super-Resolution via Deep Recursive Residual Network
Computer Vision and Pattern Recognition, 2017Recently, Convolutional Neural Network (CNN) based models have achieved great success in Single Image Super-Resolution (SISR). Owing to the strength of deep networks, these CNN models learn an effective nonlinear mapping from the low-resolution input ...
Ying Tai, Jian Yang, Xiaoming Liu
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Fast, Accurate, and, Lightweight Super-Resolution with Cascading Residual Network
European Conference on Computer Vision, 2018In recent years, deep learning methods have been successfully applied to single-image super-resolution tasks. Despite their great performances, deep learning methods cannot be easily applied to real-world applications due to the requirement of heavy ...
Namhyuk Ahn +2 more
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