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Gaussian processes are one of the dominant approaches in Bayesian learning.
Multilayer Feedforward Networks Are Universal Approximators
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Using the Nyström Method to Speed Up Kernel Machines
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Gaussian Process Latent Variable Models for Visualisation of High Dimensional Data
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Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models
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A tutorial on energy-based learning
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A First Look at Rigorous Probability Theory
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Learning for Larger Datasets with the Gaussian Process Latent Variable Model
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Hierarchical Gaussian Process Latent Variable Models
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L., Kai Li, and Li Fei-Fei (2009) · 2009
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Variational Learning of Inducing Variables in Sparse Gaussian Processes
Titsias, M. (2009) · 2009
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Bayesian Gaussian Process Latent Variable Model
Titsias, M. and Lawrence, N. D. (2010) · 2010
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Variational Gaussian Process Dynamical Systems
Damianou, A. C., Titsias, M. K., and Lawrence, N. D. (2011) · 2011
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The Theory That Would Not Die: How Bayes’ Rule Cracked the Enigma Code, Hunted Down Russian Submarines, & Emerged Triumphant from Two Centuries of C
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Introduction to Stochastic Calculus with Applications
Klebaner, F. (2012) · 2012
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Machine Learning: A Probabilistic Perspective
Murphy, K. (2012) · 2012
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Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Gal, Y. and Ghahramani, Z. (2016) · 2016
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A Review on Gaussian Process Latent Variable Models
Li, P. and Chen, S. (2016) · 2016
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Stochastic Variational Deep Kernel Learning
Wilson, A., Hu, Z., Salakhutdinov, R., and Xing, E. (2016) · 2016
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Doubly Stochastic Variational Inference for Deep Gaussian Processes
Salimbeni, H. and Deisenroth, M. (2017) · 2017
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Garnelo, M., Schwarz, J., Rosenbaum, D., Viola, F., Rezende, D. J., Eslami, S., and Teh, Y. W. (2018) · 2018
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Inference in Deep Gaussian Processes Using Stochastic Gradient Hamiltonian Monte Carlo
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Stochastic Gradient Hamiltonian Monte Carlo
Chen, T., Fox, E., and Guestrin, C. (2014) · 2014
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Weight Uncertainty in Neural Network
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Variational Auto-encoded Deep Gaussian Processes
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Deep Gaussian Processes and Variational Propagation of Pncertainty
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Havasi, M., Hernández-Lobato, J. M., and Murillo-Fuentes, J. J. (2018) · 2018
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Deep Neural Networks as Gaussian Processes
Lee, J., Sohl-dickstein, J., Pennington, J., Novak, R., Schoenholz, S., and Bahri, Y. (2018) · 2018
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The Book of Why: The New Science of Cause and Effect
Pearl, J. and Mackenzie, D. (2018) · 2018
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Attentive Neural Processes
Kim, H., Mnih, A., Schwarz, J., Garnelo, M., Eslami, A., Rosenbaum, D., Vinyals, O., and Teh, Y. W. (2019) · 2019
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When Gaussian Process Meets Big Data: A Review of Scalable GPs
Liu, H., Ong, Y.-S., Shen, X., and Cai, J. (2020) · 2020
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Continual Learning with Hypernetworks
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Energy-based processes for exchangeable data
Yang, M., Dai, B., Dai, H., and Schuurmans, D. (2020) · 2020
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