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Despite advances in scalable models, the inference tools used for Gaussian processes (GPs) have yet to fully capitalize on developments in computing hardware.
An iteration method for the solution of the eigenvalue problem of linear differential and integral operators
C. Lanczos · 1950
Earlier work this paper cites.
Practical use of the symmetric Lanczos process with re-orthogonalization
C. Paige · 1970
Earlier work this paper cites.
The block conjugate gradient algorithm and related methods
D. P. O’Leary · 1980
Earlier work this paper cites.
A new look at the Lanczos algorithm for solving symmetric systems of linear equations
B. N. Parlett · 1980
Earlier work this paper cites.
A stochastic estimator of the trace of the influence matrix for laplacian smoothing splines
M. F. Hutchinson · 1990
Earlier work this paper cites.
Applied numerical linear algebra , volume 56
J. W. Demmel · 1997
Earlier work this paper cites.
Flat minima
S. Hochreiter and J. Schmidhuber · 1997
Earlier work this paper cites.
Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit
R. H. Hahnloser, R. Sarpeshkar, M. A. Mahowald, R. J. Douglas, and H. S. Seung · 2000
Earlier work this paper cites.
Iterative methods for sparse linear systems , volume 82
Y. Saad · 2003
Earlier work this paper cites.
Iterative Krylov methods for large linear systems , volume 13
H. A. Van der Vorst · 2003
Earlier work this paper cites.
A unifying view of sparse approximate Gaussian process regression
J. Quiñonero-Candela and C. E. Rasmussen · 2005
Earlier work this paper cites.
Gaussian processes for machine learning , volume 1
C. E. Rasmussen and C. K. Williams · 2006
Earlier work this paper cites.
Sparse Gaussian processes using pseudo-inputs
E. Snelson and Z. Ghahramani · 2006
Earlier work this paper cites.
Multi-task Gaussian process prediction
E. V. Bonilla, K. M. Chai, and C. Williams · 2008
Earlier work this paper cites.
Fast Gaussian process methods for point process intensity estimation
J. P. Cunningham, K. V. Shenoy, and M. Sahani · 2008
Earlier work this paper cites.
Matrices, moments and quadrature with applications
G. H. Golub and G. Meurant · 2009
Earlier work this paper cites.
Gaussian processes and fast matrix-vector multiplies
I. Murray · 2009
Earlier work this paper cites.
Variational learning of inducing variables in sparse Gaussian processes
M. K. Titsias · 2009
Cited alongside, same era.
Large-scale machine learning with stochastic gradient descent
L. Bottou · 2010
Cited alongside, same era.
Numerical linear algebra and applications , volume 116
B. N. Datta · 2010
Cited alongside, same era.
Randomized algorithms for estimating the trace of an implicit symmetric positive semi-definite matrix
H. Avron and S. Toledo · 2011
Cited alongside, same era.
Matrix computations , volume 3
G. H. Golub and C. F. Van Loan · 2012
Cited alongside, same era.
On the low-rank approximation by the pivoted cholesky decomposition
H. Harbrecht, M. Peters, and R. Schneider · 2012
Cited alongside, same era.
Thoughts on massively scalable Gaussian processes
A. G. Wilson, C. Dann, and H. Nickisch · 2015
Later among the works it cites.
Tensorflow: A system for large-scale machine learning
M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, et al · 2016
Later among the works it cites.
Entropy-sgd: Biasing gradient descent into wide valleys
P. Chaudhari, A. Choromanska, S. Soatto, Y. LeCun, C. Baldassi, C. Borgs, J. Chayes, L. Sagun, and R. Zecchina · 2016
Later among the works it cites.
Preconditioning kernel matrices
K. Cutajar, M. Osborne, J. Cunningham, and M. Filippone · 2016
Later among the works it cites.
Improved stochastic trace estimation using mutually unbiased bases
J. K. Fitzsimons, M. A. Osborne, S. J. Roberts, and J. F. Fitzsimons · 2016
Later among the works it cites.
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Y. Saatçi · 2012
Cited alongside, same era.
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Cited alongside, same era.
Gaussian processes for big data
J. Hensman, N. Fusi, and N. D. Lawrence · 2013
Cited alongside, same era.
Caffe: Convolutional architecture for fast feature embedding
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell · 2014
Cited alongside, same era.
Auto-encoding variational Bayes
D. P. Kingma and M. Welling · 2014
Cited alongside, same era.
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A. G. Wilson · 2014
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Later among the works it cites.
On large-batch training for deep learning: Generalization gap and sharp minima
N. S. Keskar, D. Mudigere, J. Nocedal, M. Smelyanskiy, and P. T. P. Tang · 2016
Later among the works it cites.
Scalable log determinants for Gaussian process kernel learning
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Later among the works it cites.
Densely connected convolutional networks
G. Huang, Z. Liu, K. Q. Weinberger, and L. van der Maaten · 2017
Later among the works it cites.
Gpflow: A Gaussian process library using TensorFlow
A. G. d. G. Matthews, M. van der Wilk, T. Nickson, K. Fujii, A. Boukouvalas, P. León-Villagrá, Z. Ghahramani, and J. Hensman · 2017
Later among the works it cites.
Automatic differentiation in PyTorch
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer · 2017
Later among the works it cites.
Fast estimation of tr (f (a)) via stochastic Lanczos quadrature
S. Ubaru, J. Chen, and Y. Saad · 2017
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Uci machine learning repository
A. Asuncion and D. Newman · 2018
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Product kernel interpolation for scalable Gaussian processes
J. R. Gardner, G. Pleiss, R. Wu, K. Q. Weinberger, and A. G. Wilson · 2018
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Averaging weights leads to wider optima and better generalization
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Constant-time predictive distributions for Gaussian processes
G. Pleiss, J. R. Gardner, K. Q. Weinberger, and A. G. Wilson · 2018
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