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This paper introduces the hypervolume maximization with a single solution as an alternative to the mean loss minimization.
Support-vector networks
Cortes, C. and Vapnik, V · 1995
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Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
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Performance Assessment of Multiobjective Optimizers: An Analysis and Review
Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C. M., and Da Fonseca, V. G · 2003
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Boyd, S. P. and Vandenberghe, L · 2004
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A critical review of multi-objective optimization in data mining: A position paper
Freitas, A. A · 2004
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Bishop, C. M · 2006
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Chandra, A. and Yao, X · 2006
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Pareto-, aggregation-, and indicator-based methods in many-objective optimization
Wagner, T., Beume, N., and Naujoks, B · 2007
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Zitzler, E., Brockhoff, D., and Thiele, L · 2007
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Theory of the hypervolume indicator: optimal μ \mu -distributions and the choice of the reference point
Auger, A., Bader, J., Brockhoff, D., and Zitzler, E · 2009
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Bengio, Y · 2009
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Beume, N., Fonseca, C. M., López-Ibáñez, M., Paquete, L., and Vahrenhold, J · 2009
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Probabilistic Graphical Models: Principles and Techniques
Adadelta: An adaptive learning rate method
Zeiler, M. D · 2012
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Auto-encoding variational Bayes
Kingma, D. P. and Welling, M · 2013
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Multi-objective optimization
Deb, K · 2014
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Equilibrated adaptive learning rates for non-convex optimization
Dauphin, Y., de Vries, H., and Bengio, Y · 2015
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Distributional smoothing by virtual adversarial examples
Miyato, T., Maeda, S., Koyama, M., Nakae, K., and Ishii, S · 2015
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Adaptive subgradient methods for online learning and stochastic optimization
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Rifai, S., Vincent, P., Muller, X., Glorot, X., and Bengio, Y · 2011
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Deep learning
Goodfellow, I., Bengio, Y., and Courville, A · 2016
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