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The focus of this paper is on intrinsic methods to detect overfitting.
Approximate statistical tests for comparing supervised classification learning algorithms
Dietterich, T. G · 1998
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Bernier, J., Ortega, J., Ros Vidal, E., Rojas, I., and Prieto, A · 2001
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Zielinski, P., Krishnan, S., and Chatterjee, S · 2003
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On Algorithms for Technology Mapping
Chatterjee, S · 2007
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MNIST handwritten digit database
LeCun, Y. and Cortes, C · 2010
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Scikit-learn: Machine learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E · 2011
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Spectrally-normalized margin bounds for neural networks
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Sharp minima can generalize for deep nets
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Xiao, H., Rasul, K., and Vollgraf, R · 2017
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Arora, S., Ge, R., Neyshabur, B., and Zhang, Y · 2018
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Towards understanding the role of over-parametrization in generalization of neural networks
Neyshabur, B., Li, Z., Bhojanapalli, S., LeCun, Y., and Srebro, N · 2018
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A Scale Invariant Flatness Measure for Deep Network Minima
Rangamani, A., Nguyen, N. H., Kumar, A., Phan, D., Chin, S. H., and Tran, T. D · 2019
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Aiger library and tools, 2007
Biere, A · 2020
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Coherent gradients: An approach to understanding generalization in gradient descent-based optimization
Chatterjee, S · 2020
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Understanding deep learning requires rethinking generalization
Zhang, C., Bengio, S., Hardt, M., Recht, B., and Vinyals, O · 2017
Cited alongside, same era.