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We introduce scalable deep kernels, which combine the structural properties of deep learning architectures with the non-parametric flexibility of kernel methods.
A unifying view of sparse approximate gaussian process regression
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Hinton, G. E., Deng, L., Yu, D., Dahl, G. E., rahman Mohamed, A., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T. N., and Kingsbury, B. (2012) · 2012
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Unifying visual-semantic embeddings with multimodal neural language models
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Automatic construction and Natural-Language description of nonparametric regression models
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Covariance kernels for fast automatic pattern discovery and extrapolation with Gaussian processes
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Yang, Z., Moczulski, M., Denil, M., de Freitas, N., Smola, A., Song, L., and Wang, Z. (2014) · 2014
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Scalable gaussian process regression using deep neural networks
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Show, attend and tell: Neural image caption generation with visual attention
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