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We introduce an exploratory study on Mutation Validation (MV), a model validation method using mutated training labels for supervised learning.
Using additive noise in back-propagation training
Lasse Holmstrom, Petri Koistinen, et al · 1992
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Uci machine learning repository, 2007
Arthur Asuncion and David Newman · 2007
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The rademacher complexity of co-regularized kernel classes
David S Rosenberg and Peter L Bartlett · 2007
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An analysis and survey of the development of mutation testing
Yue Jia and Mark Harman · 2010
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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Lstm: A search space odyssey
Klaus Greff, Rupesh K Srivastava, Jan Koutník, Bas R Steunebrink, and Jürgen Schmidhuber · 2016
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A survey on metamorphic testing
Sergio Segura, Gordon Fraser, Ana B Sanchez, and Antonio Ruiz-Cortés · 2016
Cited alongside, same era.
Robust loss functions under label noise for deep neural networks
Aritra Ghosh, Himanshu Kumar, and PS Sastry · 2017
Cited alongside, same era.
Comparison of bayesian predictive methods for model selection
Juho Piironen and Aki Vehtari · 2017
Cited alongside, same era.
Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2017
Cited alongside, same era.
Foundations of Machine Learning
Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar · 2018
Cited alongside, same era.
Classifier Comparison, 2019
Classifier Comparison · 2019
Cited alongside, same era.
The advantages of multiple classes for reducing overfitting from test set reuse
Vitaly Feldman, Roy Frostig, and Moritz Hardt · 2019
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Limitations of bayesian leave-one-out cross-validation for model selection
Quentin F Gronau and Eric-Jan Wagenmakers · 2019
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Mutation testing advances: an analysis and survey
Mike Papadakis, Marinos Kintis, Jie Zhang, Yue Jia, Yves Le Traon, and Mark Harman · 2019
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Metamorphic testing: a new approach for generating next test cases
Tsong Y Chen, Shing C Cheung, and Shiu Ming Yiu · 2020
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Problems and opportunities in training deep learning software systems: An analysis of variance
Hung Viet Pham, Shangshu Qian, Jiannan Wang, Thibaud Lutellier, Jonathan Rosenthal, Lin Tan, Yaoliang Yu, and Nachiappan Nagappan · 2020
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Scikit-learn:SVM · 2020
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