Fetching the paper…
Reading the bibliography…
Deep Bayesian neural networks (BNNs) are a powerful tool, though computationally demanding, to perform parameter estimation while jointly estimating uncertainty around predictions.
R. E. Kass and A. E. Raftery. Bayes Factors
1995
Earlier work this paper cites.
LeCun, Yann, et al. Gradient-based learning applied to document recognition
1998
Earlier work this paper cites.
H. K. Lee. Priors for neural networks
2004
Earlier work this paper cites.
V. Nair and G. E. Hinton. Rectified linear units improve restricted boltzmann machines
2010
Earlier work this paper cites.
A. Graves. Practical variational inference for neural networks
2011
Earlier work this paper cites.
D. M. Powers. Evaluation: from precision, recall and f-measure to roc, informedness, markedness and correlation
2011
Earlier work this paper cites.
R. M. Neal. Bayesian learning for neural networks
2012
Earlier work this paper cites.
A. Gelman, J. B. Carlin, H. S. Stern, and D. B. Rubin. Bayesian Data Analysis
2013
Earlier work this paper cites.
M. D. Hoffman and A. Gelman. The no-u-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo
2014
Earlier work this paper cites.
D. P. Kingma and J. Ba. Adam: A method for stochastic optimization
2014
Earlier work this paper cites.
C. Blundell, J. Cornebise, K. Kavukcuoglu, and D. Wierstra. Weight uncertainty in neural networks
2015
Cited alongside, same era.
Y. LeCun, Y. Bengio, and G. Hinton. Deep learning
2015
Cited alongside, same era.
A. Severyn and A. Moschitti. Twitter sentiment analysis with deep convolutional neural networks
2015
Cited alongside, same era.
J. Zhou and O. G. Troyanskaya. Predicting effects of noncoding variants with deep learning–based sequence model
2015
Cited alongside, same era.
D. Amodei, C. Olah, J. Steinhardt, P. Christiano, J. Schulman, and D. Mane. Concrete problems in ai safety
2016
Cited alongside, same era.
Y. Gal and Z. Ghahramani. Dropout as a Bayesian approximation: Representing model uncertainty in deep learning
N. G. Polson, V. Sokolov, et al. Deep learning: a Bayesian perspective
2017
Later among the works it cites.
G. Zhang, S. Sun, D. Duvenaud, and R. Grosse. Noisy natural gradient as variational inference
2017
Later among the works it cites.
Y. Zhang, B. D. Aevermann, T. K. Anderson, D. F. Burke, G. Dauphin, Z. Gu, S. He, S. Kumar, C. N. Larsen, A. J. Lee, et al. Influenza research database: an integrated bioinformatics resource for influenza virus research
2017
Later among the works it cites.
2018
Later among the works it cites.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, et al. Pytorch: An imperative style, high-performance deep learning library
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2016
Cited alongside, same era.
I. Goodfellow, Y. Bengio, and A. Courville. Deep Learning
2016
Cited alongside, same era.
D. Hendrycks and K. Gimpel. A baseline for detecting misclassified and out-of- distribution examples in neural networks
2016
Cited alongside, same era.
W. Bao, J. Yue, and Y. Rao. A deep learning framework for financial time series using stacked autoencoders and long-short term memory
2017
Cited alongside, same era.
B. Lakshminarayanan, A. Pritzel, and C. Blundell. Simple and scalable predictive uncertainty estimation using deep ensembles
2017
Cited alongside, same era.
2019
Later among the works it cites.
D. Silvestro, S. Castiglione, A. Mondanaro, C. Serio, M. Melchionna, P. Piras, M. Di Febbraro, F. Carotenuto, L. Rook, and P. Raia. A 450 million years long latitu- dinal gradient in age-dependent extinction
2019
Later among the works it cites.
M. Wang, L. Yu, D. Zheng, Q. Gan, Y. Gai, Z. Ye, M. Li, J. Zhou, Q. Huang, C. Ma, et al. Deep graph library: Towards efficient and scalable deep learning on graphs
2019
Later among the works it cites.
R. Zhang, C. Li, J. Zhang, C. Chen, and A. G. Wilson. Cyclical stochastic gradient mcmc for bayesian deep learning
2019
Later among the works it cites.
S. Wang, B. Kang, J. Ma, X. Zeng, M. Xiao, J. Guo, M. Cai, J. Yang, Y. Li, X. Meng, et al. A deep learning algorithm using ct images to screen for corona virus disease (covid-19)
2020
Closest in time.
F. Wenzel, K. Roth, B. S. Veeling, J. Swiatkowski, L. Tran, S. Mandt, J. Snoek, T. Salimans, R. Jenatton, and S. Nowozin. How good is the bayes posterior in deep neural networks really?
2020
Closest in time.