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Deep Gaussian Processes (DGPs) combine the expressiveness of Deep Neural Networks (DNNs) with quantified uncertainty of Gaussian Processes (GPs).
Deep gaussian processes with importance-weighted variational inference
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Structure discovery in nonparametric regression through compositional kernel search
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Fast dropout training
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Exponential expressivity in deep neural networks through transient chaos
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Deep kernel learning
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Understanding deep learning requires rethinking generalization
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Random feature expansions for deep gaussian processes
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Deep neural networks as gaussian processes
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Avoiding pathologies in very deep networks
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Convolutional kernel networks
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Student-t processes as alternatives to gaussian processes
Shah, A., Wilson, A., and Ghahramani, Z. (2014) · 2014
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Fast kernel learning for multidimensional pattern extrapolation
Wilson, A. G., Gilboa, E., Nehorai, A., and Cunningham, J. P. (2014) · 2014
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Deep gaussian processes for regression using approximate expectation propagation
Bui, T., Hernández-Lobato, D., Hernandez-Lobato, J., Li, Y., and Turner, R. (2016) · 2016
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Toward deeper understanding of neural networks: The power of initialization and a dual view on expressivity
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Doubly stochastic variational inference for deep gaussian processes
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Convolutional gaussian processes
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To understand deep learning we need to understand kernel learning
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How deep are deep gaussian processes?
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Inference in deep gaussian processes using stochastic gradient hamiltonian monte carlo
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The dynamics of learning: a random matrix approach
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A mean field view of the landscape of two-layer neural networks
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Differentiable compositional kernel learning for gaussian processes
Sun, S., Zhang, G., Wang, C., Zeng, W., Li, J., and Grosse, R. (2018) · 2018
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