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This paper investigates a novel algorithmic approach to data representation based on kernel methods.
Hilbert spaces of operator-valued functions
Senkene, E. and Tempel’man, A. (1973) · 1973
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Probabilistic methods in the geometry of banach spaces
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Auto-association by multilayer perceptrons and singular value decomposition
Bourlard, H. and Kamp, Y. (1988) · 1988
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Probability in Banach Spaces: Isoperimetry and Processes
Ledoux, M. and Talagrand, M. (1991) · 1991
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Kernel principal component analysis
Schölkopf, B., Smola, A., and Müller, K.-R. (1997) · 1997
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Nonlinear component analysis as a kernel eigenvalue problem
Schölkopf, B., Smola, A., and Müller, K.-R. (1998) · 1998
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Learning with kernels
Smola, A. J. and Schölkopf, B. (1998) · 1998
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Sparse greedy matrix approximation for machine learning
Smola, A. J. and Schölkopf, B. (2000) · 2000
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On learning vector-valued functions
Micchelli, C. A. and Pontil, M. (2005) · 2005
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Universal multitask kernels
Caponnetto, A., Micchelli, C. A., , M., and Ying, Y. (2008) · 2008
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Deep boltzmann machines
Salakhutdinov, R. and Hinton, G. (2009) · 2009
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Structured output prediction of anti-cancer drug activity
Su, H., Heinonen, M., and Rousu, J. (2010) · 2010
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Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., and Manzagol, P.-A. (2010) · 2010
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Convex analysis and monotone operator theory in Hilbert spaces
Bauschke, H. H., Combettes, P. L., et al. (2011) · 2011
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Scikit-learn: Machine learning in Python
Pedregosa, F. et al. (2011) · 2011
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Kernels for vector-valued functions: A review
Álvarez, M. A., Rosasco, L., and Lawrence, N. D. (2012) · 2012
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What regularized auto-encoders learn from the data-generating distribution
Alain, G. and Bengio, Y. (2014) · 2014
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A chain rule for the expected suprema of gaussian processes
Maurer, A. (2014) · 2014
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Keras, https://keras.io
Chollet, F. et al. (2015) · 2015
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Kernel autoencoder for semi-supervised hashing
Gholami, B. and Hajisami, A. (2016) · 2016
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Operator-valued kernels for learning from functional response data
Kadri, H., Duflos, E., Preux, P., Canu, S., Rakotomamonjy, A., and Audiffren, J. (2016) · 2016
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Variational graph autoencoders
Kipf, T. N. and Welling, M. (2016) · 2016
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Autoencoders, unsupervised learning, and deep architectures
Baldi, P. (2012) · 2012
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Foundations of Machine Learning
Mohri, M., Rostamizadeh, A., and Talwalkar, A. (2012) · 2012
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Representation learning: a review and new perspectives
Bengio, Y., Courville, A., Vincent, P., and Umanità, V. (2013) · 2013
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Fast metabolite identification with input output kernel regression
Brouard, C., Shen, H., Dührkop, K., d’Alché-Buc, F., Böcker, S., and Rousu, J. (2016a)
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Input output kernel regression: Supervised and semi-supervised structured output prediction with operator-valued kernels
Brouard, C., Szafranski, M., and d’Alché-Buc, F. (2016b)
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A vector-contraction inequality for rademacher complexities
Maurer, A. (2016) · 2016
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Bounds for vector-valued function estimation
Maurer, A. and Pontil, M. (2016) · 2016
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Deep kernelized autoencoders
Kampffmeyer, M., Løkse, S., Bianchi, F. M., Jenssen, R., and Livi, L. (2017) · 2017
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