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Modern deep learning methods constitute incredibly powerful tools to tackle a myriad of challenging problems.
1903
Earlier work this paper cites.
T. Tran, T.-T. Do, I. Reid, and G. Carneiro, “Bayesian generative active deep learning,”
1904
Earlier work this paper cites.
Q. Xie, Z. Dai, E. H. Hovy, M. Luong, and Q. V. Le, “Unsupervised data augmentation,”
1904
Earlier work this paper cites.
F. Galton, “Vox Populi,”
1907
Earlier work this paper cites.
1911
Earlier work this paper cites.
C. E. Shannon, “A mathematical theory of communication,”
1948
Earlier work this paper cites.
S. Kullback and R. A. Leibler, “On information and sufficiency,”
1951
Earlier work this paper cites.
W. K. Hastings, “Monte Carlo sampling methods using Markov chains and their applications,”
1970
Earlier work this paper cites.
D. J. C. MacKay, “A practical Bayesian framework for backpropagation networks,”
1992
Earlier work this paper cites.
E. I. George, G. Casella, and E. I. George, “Explaining the Gibbs sampler,”
1992
Earlier work this paper cites.
W. L. Buntine, “Operations for learning with graphical models,”
1994
Earlier work this paper cites.
S. Chib and E. Greenberg, “Understanding the Metropolis-Hastings algorithm,”
1995
Earlier work this paper cites.
L. Breiman, “Bagging predictors,”
1996
Earlier work this paper cites.
D. H. Wolpert, “The lack of a priori distinctions between learning algorithms,”
1996
Earlier work this paper cites.
M. Opper and O. Winther, “A Bayesian approach to on-line learning,”
1998
Earlier work this paper cites.
Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,”
1998
Earlier work this paper cites.
J. Lampinen and A. Vehtari, “Bayesian approach for neural networks—review and case studies,”
2001
Earlier work this paper cites.
F. Aminian and M. Aminian, “Fault diagnosis of analog circuits using Bayesian neural networks with wavelet transform as preprocessor,”
2001
Earlier work this paper cites.
2001
Earlier work this paper cites.
O. Chapelle, J. Weston, L. Bottou, and V. Vapnik, “Vicinal risk minimization,” in
2001
Earlier work this paper cites.
Z. Ghahramani and M. J. Beal, “Propagation algorithms for variational Bayesian learning,” in
2001
Earlier work this paper cites.
2001
Earlier work this paper cites.
2002
Earlier work this paper cites.
2002
Earlier work this paper cites.
A. C. Tommi and T. Jaakkola, “On information regularization,” in
2003
Earlier work this paper cites.
D. M. Titterington, “Bayesian methods for neural networks and related models,”
2004
Earlier work this paper cites.
2004
Earlier work this paper cites.
2005
Earlier work this paper cites.
E. Snelson and Z. Ghahramani, “Compact approximations to Bayesian predictive distributions,” in
2005
Earlier work this paper cites.
2005
Earlier work this paper cites.
M. Belkin, P. Niyogi, and V. Sindhwani, “Manifold regularization: A geometric framework for learning from labeled and unlabeled examples,”
2006
Earlier work this paper cites.
M. Belkin, P. Niyogi, and V. Sindhwani, “Manifold regularization: A geometric framework for learning from labeled and unlabeled examples,”
2006
Earlier work this paper cites.
C. Robert,
2007
Earlier work this paper cites.
T. Auld, A. W. Moore, and S. F. Gull, “Bayesian neural networks for internet traffic classification,”
2007
Earlier work this paper cites.
S. M. Bateni, D.-S. Jeng, and B. W. Melville, “Bayesian neural networks for prediction of equilibrium and time-dependent scour depth around bridge piers,”
2007
Earlier work this paper cites.
A. D. Kiureghian and O. Ditlevsen, “Aleatory or epistemic? does it matter?”
2009
Earlier work this paper cites.
X. Zhang, F. Liang, R. Srinivasan, and M. Van Liew, “Estimating uncertainty of streamflow simulation using bayesian neural networks,”
2009
Earlier work this paper cites.
S. J. Pan and Q. Yang, “A survey on transfer learning,”
2010
Earlier work this paper cites.
S. Yu, B. Krishnapuram, R. Rosales, and R. B. Rao, “Bayesian co-training,”
2011
Earlier work this paper cites.
R. M. Neal
2011
Cited alongside, same era.
A. Graves, “Practical variational inference for neural networks,” in
2011
Cited alongside, same era.
M. Welling and Y. W. Teh, “Bayesian learning via stochastic gradient Langevin dynamics,” in
2011
Cited alongside, same era.
Z.-H. Zhou,
2012
Cited alongside, same era.
K. P. Murphy,
2012
Cited alongside, same era.
D. Ciregan, U. Meier, and J. Schmidhuber, “Multi-column deep neural networks for image classification,” in
2012
Cited alongside, same era.
K. Janocha and W. M. Czarnecki, “On loss functions for deep neural networks in classification,”
2017
Later among the works it cites.
Q. Rao and J. Frtunikj, “Deep learning for self-driving cars: Chances and challenges,” in
2018
Later among the works it cites.
J. Ker, L. Wang, J. Rao, and T. Lim, “Deep learning applications in medical image analysis,”
2018
Later among the works it cites.
H. M. D. Kabir, A. Khosravi, M. A. Hosen, and S. Nahavandi, “Neural network-based uncertainty quantification: A survey of methodologies and applications,”
2018
Later among the works it cites.
A. Etz, Q. F. Gronau, F. Dablander, P. A. Edelsbrunner, and B. Baribault, “How to become a Bayesian in eight easy steps: An annotated reading list,”
2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2013
Cited alongside, same era.
N. Natarajan, I. S. Dhillon, P. K. Ravikumar, and A. Tewari, “Learning with noisy labels,” in
2013
Cited alongside, same era.
D.-H. Lee, “Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks,” in
2013
Cited alongside, same era.
M. D. Hoffman, D. M. Blei, C. Wang, and J. Paisley, “Stochastic variational inference,”
2013
Cited alongside, same era.
D. P. Kingma and M. Welling, “Stochastic gradient vb and the variational auto-encoder,” in
2014
Cited alongside, same era.
B. Frenay and M. Verleysen, “Classification in the presence of label noise: A survey,”
2014
Cited alongside, same era.
S. Depeweg, J.-M. Hernandez-Lobato, F. Doshi-Velez, and S. Udluft, “Decomposition of uncertainty in Bayesian deep learning for efficient and risk-sensitive learning,” in
2018
Later among the works it cites.
H. Ritter, A. Botev, and D. Barber, “Online structured Laplace approximations for overcoming catastrophic forgetting,” in
2018
Later among the works it cites.
S. Pouyanfar, S. Sadiq, Y. Yan, H. Tian, Y. Tao, M. P. Reyes, M.-L. Shyu, S.-C. Chen, and S. S. Iyengar, “A survey on deep learning: Algorithms, techniques, and applications,”
2018
Later among the works it cites.
Y. Wen, P. Vicol, J. Ba, D. Tran, and R. Grosse, “Flipout: Efficient pseudo-independent weight perturbations on mini-batches,” in
2018
Later among the works it cites.
E. Grant, C. Finn, S. Levine, T. Darrell, and T. L. Griffiths, “Recasting gradient-based meta-learning as hierarchical Bayes,” in
2018
Later among the works it cites.
H. Ritter, A. Botev, and D. Barber, “A scalable laplace approximation for neural networks,” in
2018
Later among the works it cites.
M. Khan, D. Nielsen, V. Tangkaratt, W. Lin, Y. Gal, and A. Srivastava, “Fast and scalable Bayesian deep learning by weight-perturbation in Adam,” in
2018
Later among the works it cites.
2018
Later among the works it cites.
K.-C. Wang, P. Vicol, J. Lucas, L. Gu, R. Grosse, and R. Zemel, “Adversarial distillation of Bayesian neural network posteriors,” in
2018
Later among the works it cites.
V. Kuleshov, N. Fenner, and S. Ermon, “Accurate uncertainties for deep learning using calibrated regression,” in
2018
Later among the works it cites.
J. Nixon, M. W. Dusenberry, L. Zhang, G. Jerfel, and D. Tran, “Measuring calibration in deep learning,” in
2019
Later among the works it cites.
J. Mitros and B. M. Namee, “On the validity of Bayesian neural networks for uncertainty estimation,” in
2019
Later among the works it cites.
Y. Ovadia, E. Fertig, J. Ren, Z. Nado, D. Sculley, S. Nowozin, J. Dillon, B. Lakshminarayanan, and J. Snoek, “Can you trust your model's uncertainty? evaluating predictive uncertainty under dataset shift,” in
2019
Later among the works it cites.
A. D. Cobb, M. D. Himes, F. Soboczenski, S. Zorzan, M. D. O’Beirne, A. G. Baydin, Y. Gal, S. D. Domagal-Goldman, G. N. Arney, and D. A. and, “An ensemble of bayesian neural networks for exoplanetary atmospheric retrieval,”
2019
Later among the works it cites.
Z. Li, B. Ko, and H.-J. Choi, “Naive semi-supervised deep learning using pseudo-label,”
2019
Later among the works it cites.
L. Beyer, X. Zhai, A. Oliver, and A. Kolesnikov, “S4L: Self-supervised semi-supervised learning,” in
2019
Later among the works it cites.
W. J. Maddox, P. Izmailov, T. Garipov, D. P. Vetrov, and A. G. Wilson, “A simple baseline for Bayesian uncertainty in deep learning,” in
2019
Later among the works it cites.
D. P. Kingma, M. Welling
2019
Later among the works it cites.
E. Goan and C. Fookes,
2020
Closest in time.
H. Wang and D.-Y. Yeung, “A survey on bayesian deep learning,”
2020
Closest in time.
X. Zhang and S. Mahadevan, “Bayesian neural networks for flight trajectory prediction and safety assessment,”
2020
Closest in time.
W. Beker, A. Wołos, S. Szymkuć, and B. A. Grzybowski, “Minimal–uncertainty prediction of general drug–likeness based on Bayesian neural networks,”
2020
Closest in time.
A. Gelman and other Stan developers, “Prior choice recommendations,” 2020, retrieved from https://github.com/stan-dev/stan/wiki/Prior-Choice-Recommendations [last seen 13.07.2020]
2020
Closest in time.
M. S. Bari, M. T. Mohiuddin, and S. Joty, “MultiMix: A robust data augmentation strategy for cross-lingual nlp,” in
2020
Closest in time.
2020
Closest in time.
L. Jing and Y. Tian, “Self-supervised visual feature learning with deep neural networks: A survey,”
2020
Closest in time.
H. Laga, L. V. Jospin, F. Boussaid, and M. Bennamoun, “A survey on deep learning techniques for stereo-based depth estimation,”
2020
Closest in time.
A. Chan, A. Alaa, Z. Qian, and M. Van Der Schaar, “Unlabelled data improves Bayesian uncertainty calibration under covariate shift,” in
2020
Closest in time.
T. Pearce, F. Leibfried, A. Brintrup, M. Zaki, and A. Neely, “Uncertainty in neural networks: Approximately Bayesian ensembling,” in
2020
Closest in time.
S. C.-H. Yang, W. K. Vong, R. B. Sojitra, T. Folke, and P. Shafto, “Mitigating belief projection in explainable artificial intelligence via bayesian teaching,”
2021
Closest in time.
2021
Closest in time.
X.-F. Han, H. Laga, and M. Bennamoun, “Image-based 3d object reconstruction: State-of-the-art and trends in the deep learning era,”
2021
Closest in time.
J. Hron, A. Matthews, and Z. Ghahramani, “Variational Bayesian dropout: pitfalls and fixes,” in
2028
Closest in time.
Y. Li and Y. Gal, “Dropout inference in Bayesian neural networks with alpha-divergences,” in
2061
Closest in time.