Deep kernel processes
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[1] Alexandre Lacoste,et al. PAC-Bayesian Theory Meets Bayesian Inference , 2016, NIPS.
[2] Laurence Aitchison. Why bigger is not always better: on finite and infinite neural networks , 2020, ICML.
[3] A. Dawid. Some matrix-variate distribution theory: Notational considerations and a Bayesian application , 1981 .
[4] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[5] Andrew Gordon Wilson,et al. Generalised Wishart Processes , 2010, UAI.
[6] Taras Bodnar,et al. Singular inverse Wishart distribution and its application to portfolio theory , 2016, J. Multivar. Anal..
[7] Carl Edward Rasmussen,et al. Approximate Inference for Fully Bayesian Gaussian Process Regression , 2019, AABI.
[8] Pat H. Sterbenz,et al. Floating-point computation , 1973 .
[9] Jaehoon Lee,et al. Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes , 2018, ICLR.
[10] M. L. Eaton. Multivariate statistics : a vector space approach , 1985 .
[11] Michael Rabadi,et al. Kernel Methods for Machine Learning , 2015 .
[12] Oliver Pfaffel. Wishart Processes , 2012, 1201.3256.
[13] Taras Bodnar,et al. Properties of the singular, inverse and generalized inverse partitioned Wishart distributions , 2008 .
[14] Jaehoon Lee,et al. Deep Neural Networks as Gaussian Processes , 2017, ICLR.
[15] Richard E. Turner,et al. Two problems with variational expectation maximisation for time-series models , 2011 .
[16] David A. Moore. Symmetrized Variational Inference , 2016 .
[17] David J. C. MacKay,et al. A Practical Bayesian Framework for Backpropagation Networks , 1992, Neural Computation.
[18] M. Glickman,et al. Multivariate Stochastic Volatility via Wishart Processes , 2006 .
[19] C. Gouriéroux,et al. Derivative Pricing With Wishart Multivariate Stochastic Volatility , 2010 .
[20] Linda R. Petzold,et al. Improving the Identifiability of Neural Networks for Bayesian Inference , 2017 .
[21] Muni S. Srivastava,et al. Singular Wishart and multivariate beta distributions , 2003 .
[22] Lawrence K. Saul,et al. Kernel Methods for Deep Learning , 2009, NIPS.
[23] Richard E. Turner,et al. Gaussian Process Behaviour in Wide Deep Neural Networks , 2018, ICLR.
[24] Neil D. Lawrence,et al. Deep Gaussian Processes , 2012, AISTATS.
[25] Martin Jorgensen,et al. Stochastic Differential Equations with Variational Wishart Diffusions , 2020, ICML.
[26] Julien Cornebise,et al. Weight Uncertainty in Neural Networks , 2015, ArXiv.
[27] Mark van der Wilk,et al. Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processes , 2019, NeurIPS.
[28] M. McAleer,et al. The structure of dynamic correlations in multivariate stochastic volatility models , 2009 .
[29] Melih Kandemir,et al. The Deep Feed-Forward Gaussian Process: An Effective Generalization to Covariance Priors , 2015, FE@NIPS.
[30] Daan Wierstra,et al. Stochastic Backpropagation and Approximate Inference in Deep Generative Models , 2014, ICML.
[31] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[32] Andrew Gordon Wilson,et al. Student-t Processes as Alternatives to Gaussian Processes , 2014, AISTATS.
[33] Carl E. Rasmussen,et al. Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.
[34] Laurence Aitchison,et al. Deep Convolutional Networks as shallow Gaussian Processes , 2018, ICLR.
[35] H. Uhlig. On singular Wishart and singular multivariate beta distributions , 1994 .
[36] Hao Li,et al. Visualizing the Loss Landscape of Neural Nets , 2017, NeurIPS.
[37] M. Glickman,et al. Factor Multivariate Stochastic Volatility via Wishart Processes , 2006 .
[38] Zoubin Ghahramani,et al. Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning , 2015, ICML.
[39] Marc Peter Deisenroth,et al. Doubly Stochastic Variational Inference for Deep Gaussian Processes , 2017, NIPS.
[40] Alan Edelman,et al. The efficient evaluation of the hypergeometric function of a matrix argument , 2006, Math. Comput..
[41] Sebastian W. Ober,et al. Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes , 2020, ICML.