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We propose a method (TT-GP) for approximate inference in Gaussian Process (GP) models.
Cubic convolution interpolation for digital image processing
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J. Quiñonero-Candela and C. E. Rasmussen · 2005
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Efficient bounds for the softmax function and applications to approximate inference in hybrid models
G. Bouchard · 2007
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Learning multiple layers of features from tiny images
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Fast kernel learning for multidimensional pattern extrapolation
Scalable variational gaussian process classification
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
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Blitzkriging: Kronecker-structured stochastic gaussian processes
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Tensorizing neural networks
A. Novikov, D. Podoprikhin, A. Osokin, and D. Vetrov · 2015
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Kernel interpolation for scalable structured gaussian processes (kiss-gp)
A. G. Wilson and H. Nickisch · 2015
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Practical learning of deep gaussian processes via random fourier features
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A. G. Wilson, E. Gilboa, J. P. Cunningham, and A. Nehorai · 2014
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Gpstruct: Bayesian structured prediction using gaussian processes
S. Bratieres, N. Quadrianto, and Z. Ghahramani · 2015
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Deep kernel learning
A. G. Wilson, Z. Hu, R. Salakhutdinov, and E. P. Xing
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Stochastic variational deep kernel learning
A. G. Wilson, Z. Hu, R. Salakhutdinov, and E. P. Xing
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K. Cutajar, E. V. Bonilla, P. Michiardi, and M. Filippone · 2016
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GPflow: A Gaussian process library using TensorFlow
A. G. de G. Matthews, M. van der Wilk, T. Nickson, K. Fujii, A. Boukouvalas, P. León-Villagrá, Z. Ghahramani, and J. Hensman · 2016
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Tensor train decomposition on tensorflow (t3f)
A. Novikov, P. Izmailov, V. Khrulkov, M. Figurnov, and I. Oseledets · 2018
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