Fetching the paper…
Reading the bibliography…
Data augmentation is often used to incorporate inductive biases into models.
Steps toward artificial intelligence
M. Minsky · 1961
Earlier work this paper cites.
Occam’s razor
C. E. Rasmussen and Z. Ghahramani · 2001
Earlier work this paper cites.
Model comparison and Occam’s razor
D. J. C. MacKay · 2003
Earlier work this paper cites.
Gaussian processes for machine learning
C. K. I. Williams and C. E. Rasmussen · 2006
Earlier work this paper cites.
Using deep belief nets to learn covariance kernels for Gaussian processes
G. E. Hinton and R. Salakhutdinov · 2007
Earlier work this paper cites.
Group theoretical methods in machine learning
I. R. Kondor · 2008
Earlier work this paper cites.
Learning multiple layers of features from tiny images
A. Krizhevsky · 2009
Earlier work this paper cites.
Variational learning of inducing variables in sparse gaussian processes
M. Titsias · 2009
Earlier work this paper cites.
Two problems with variational expectation maximisation for time-series models
R. E. Turner and M. Sahani · 2011
Earlier work this paper cites.
Argumentwise invariant kernels for the approximation of invariant functions
D. Ginsbourger, X. Bay, O. Roustant, and L. Carraro · 2012
Earlier work this paper cites.
Deep Gaussian processes
A. Damianou and N. D. Lawrence · 2013
Earlier work this paper cites.
Kernels and designs for modelling invariant functions: From group invariance to additivity
D. Ginsbourger, N. Durrande, and O. Roustant · 2013
Earlier work this paper cites.
Weight uncertainty in neural networks
C. Blundell, J. Cornebise, K. Kavukcuoglu, and D. Wierstra · 2015
Earlier work this paper cites.
Scalable variational Gaussian process classification
J. Hensman, A. G. d. G. Matthews, and Z. Ghahramani · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
Cited alongside, same era.
Importance weighted autoencoders
Y. Burda, R. Grosse, and R. Salakhutdinov · 2016
Cited alongside, same era.
Manifold Gaussian processes for regression
R. Calandra, J. Peters, C. E. Rasmussen, and M. P. Deisenroth · 2016
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
On sparse variational methods and the Kullback-Leibler divergence between stochastic processes
A. G. d. G. Matthews, J. Hensman, R. E. Turner, and Z. Ghahramani · 2016
Cited alongside, same era.
J. Bradshaw, A. G. d. G. Matthews, and Z. Ghahramani · 2017
Convergence of sparse variational inference in Gaussian processes regression
D. R. Burt, C. E. Rasmussen, and M. van der Wilk · 2020
Later among the works it cites.
Randaugment: Practical automated data augmentation with a reduced search space
E. D. Cubuk, B. Zoph, J. Shlens, and Q. V. Le · 2020
Later among the works it cites.
Bayesian image classification with deep convolutional Gaussian processes
V. Dutordoir, M. van der Wilk, A. Artemev, and J. Hensman · 2020
Later among the works it cites.
Optimizing millions of hyperparameters by implicit differentiation
J. Lorraine, P. Vicol, and D. Duvenaud · 2020
Later among the works it cites.
Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes
S. W. Ober and L. Aitchison · 2020
Later among the works it cites.
Probabilistic spatial transformers for Bayesian data augmentation
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
A unifying framework for Gaussian process pseudo-point approximations using power expectation propagation
T. D. Bui, J. Yan, and R. E. Turner · 2017
Cited alongside, same era.
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
Cited alongside, same era.
Learning invariances using the marginal likelihood
M. van der Wilk, M. Bauer, S. T. John, and J. Hensman · 2018
Cited alongside, same era.
Rotation equivariant CNNs for digital pathology
B. S. Veeling, J. Linmans, J. Winkens, T. Cohen, and M. Welling · 2018
Cited alongside, same era.
Autoaugment: Learning augmentation strategies from data
E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le · 2019
Cited alongside, same era.
A kernel theory of modern data augmentation
T. Dao, A. Gu, A. Ratner, V. Smith, C. De Sa, and C. Ré · 2019
Cited alongside, same era.
P. Schwöbel, F. Warburg, M. Jørgensen, K. H. Madsen, and S. Hauberg · 2020
Later among the works it cites.
A framework for interdomain and multioutput Gaussian processes
M. van der Wilk, V. Dutordoir, S. T. John, A. Artemev, V. Adam, and J. Hensman · 2020
Later among the works it cites.
Efficiently sampling functions from Gaussian process posteriors
J. T. Wilson, V. Borovitskiy, A. Terenin, P. Mostowsky, and M. P. Deisenroth · 2020
Later among the works it cites.
Deep neural networks as point estimates for deep Gaussian processes
V. Dutordoir, J. Hensman, M. van der Wilk, C. H. Ek, Z. Ghahramani, and N. Durrande · 2021
Closest in time.
Scalable marginal likelihood estimation for model selection in deep learning
A. Immer, M. Bauer, V. Fortuin, G. Rätsch, and M. E. Khan · 2021
Closest in time.
Data augmentation in bayesian neural networks and the cold posterior effect, 2021
S. Nabarro, S. Ganev, A. Garriga-Alonso, V. Fortuin, M. van der Wilk, and L. Aitchison · 2021
Closest in time.
The promises and pitfalls of deep kernel learning
S. W. Ober, C. E. Rasmussen, and M. van der Wilk · 2021
Closest in time.
On feature collapse and deep kernel learning for single forward pass uncertainty
J. van Amersfoort, L. Smith, A. Jesson, O. Key, and Y. Gal · 2021
Closest in time.
Meta-learning symmetries by reparameterization
A. Zhou, T. Knowles, and C. Finn · 2021
Closest in time.