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The top-k error is a common measure of performance in machine learning and computer vision.
On the algorithmic implementation of multiclass kernel-based vector machines
Koby Crammer and Yoram Singer · 2001
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SSVM: A smooth support vector machine for classification
Yuh-Jye Lee and Olvi L Mangasarian · 2001
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The concave-convex procedure (CCCP)
Alan L. Yuille and Anand Rangarajan · 2002
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Learning multiple layers of features from tiny images , 2009
Alex Krizhevsky · 2009
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Smoothing and first order methods: A unified framework
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Efficient structured prediction with latent variables for general graphical models
Alexander G. Schwing, Tamir Hazan, Marc Pollefeys, and Raquel Urtasun · 2012
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Top-k multiclass SVM
Maksim Lapin, Matthias Hein, and Bernt Schiele · 2015
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ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
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Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Accurate, validated and fast evaluation of elementary symmetric functions and its application
Hao Jiang, Stef Graillat, Roberto Barrio, and Canqun Yang · 2016
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Learning with average top-k loss
Yanbo Fan, Siwei Lyu, Yiming Ying, and Bao-Gang Hu · 2017
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Mollifying networks
Caglar Gulcehre, Marcin Moczulski, Francesco Visin, and Yoshua Bengio · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Kilian Q Weinberger, and Laurens van der Maaten · 2017
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Analysis and optimization of loss functions for multiclass, top-k, and multilabel classification
Maksim Lapin, Matthias Hein, and Bernt Schiele · 2017
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Improving pairwise ranking for multi-label image classification
Yuncheng Li, Yale Song, and Jiebo Luo · 2017
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Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
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Loss functions for top-k error: Analysis and insights
Maksim Lapin, Matthias Hein, and Bernt Schiele · 2016
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Robust top-k multiclass SVM for visual category recognition
Xiaojun Chang, Yao-Liang Yu, and Yi Yang · 2017
Cited alongside, same era.
Entropy-SGD: Biasing gradient descent into wide valleys
Pratik Chaudhari, Anna Choromanska, Stefano Soatto, and Yann LeCun · 2017
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Top-k multi-class svm using multiple features
Caixia Yan, Minnan Luo, Huan Liu, Zhihui Li, and Qinghua Zheng · 2017
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