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Measuring the generalization performance of a Deep Neural Network (DNN) without relying on a validation set is a difficult task.
The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains
D. I. Shuman, S. K. Narang, P. Frossard, A. Ortega, and P. Vandergheynst · 2013
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Deep Learning
I. Goodfellow, Y. Bengio, and A. Courville · 2016
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Identity mappings in deep residual networks
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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mixup: Beyond empirical risk minimization
H. Zhang, M. Cisse, Y. N. Dauphin, and D. Lopez-Paz · 2017
Earlier work this paper cites.
An inside look at deep neural networks using graph signal processing
V. Gripon, A. Ortega, and B. Girault · 2018
Cited alongside, same era.
Predicting the accuracy of a few-shot classifier
M. Bontonou, L. Béthune, and V. Gripon · 2020
Cited alongside, same era.
In search of robust measures of generalization
G. K. Dziugaite, A. Drouin, B. Neal, N. Rajkumar, E. Caballero, L. Wang, I. Mitliagkas, and D. M. Roy · 2020
Cited alongside, same era.
Graph vertex sampling with arbitrary graph signal hilbert spaces
B. Girault, A. Ortega, and S. S. Narayayan · 2020
Cited alongside, same era.
Fantastic generalization measures and where to find them
Y. Jiang*, B. Neyshabur*, H. Mobahi, D. Krishnan, and S. Bengio · 2020
Closest in time.
Graph topology inference benchmarks for machine learning
C. Lassance, V. Gripon, and G. Mateos · 2020
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Representing deep neural networks latent space geometries with graphs
C. Lassance, V. Gripon, and A. Ortega · 2020
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Graph construction from data by non-negative kernel regression
S. Shekkizhar and A. Ortega · 2020
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