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The Bayesian paradigm has the potential to solve core issues of deep neural networks such as poor calibration and data inefficiency.
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Imagenet: A large-scale hierarchical image database
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Learning Multiple Layers of Features from Tiny Images
Krizhevsky, A. and Hinton, G · 2009
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Learning recurrent neural networks with hessian-free optimization
Martens, J. and Sutskever, I · 2011
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Reading Digits in Natural Images with Unsupervised Feature Learning
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The Fundamental Incompatibility of Scalable Hamiltonian Monte Carlo and Naive Data Subsampling
Betancourt, M · 2015
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Weight Uncertainty in Neural Networks
Blundell, C., Cornebise, J., Kavukcuoglu, K., and Wierstra, D · 2015
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Probabilistic machine learning and artificial intelligence
Ghahramani, Z · 2015
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Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification
He, K., Zhang, X., Ren, S., and Sun, J · 2015
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Probabilistic backpropagation for scalable learning of bayesian neural networks
Hernández-Lobato, J. M. and Adams, R · 2015
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Nguyen, A., Yosinski, J., and Clune, J · 2015
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Scalable bayesian optimization using deep neural networks
Snoek, J., Rippel, O., Swersky, K., Kiros, R., Satish, N., Sundaram, N., Patwary, M., Prabhat, M., and Adams, R · 2015
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Concrete Problems in AI Safety
Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., and Mané, D · 2016
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y. and Ghahramani, Z · 2016
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Deep learning , volume 1
Goodfellow, I., Bengio, Y., Courville, A., and Bengio, Y · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Structured and efficient variational deep learning with matrix gaussian posteriors
Louizos, C. and Welling, M · 2016
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Second-order optimization for neural networks
Martens, J · 2016
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Wide residual networks
Zagoruyko, S. and Komodakis, N · 2016
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A survey of model compression and acceleration for deep neural networks
Cheng, Y., Wang, D., Zhou, P., and Zhang, T · 2017
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UCI machine learning repository, 2017
Dua, D. and Graff, C · 2017
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On calibration of modern neural networks
Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q · 2017
Practical deep learning with Bayesian principles
Osawa, K., Swaroop, S., Jain, A., Eschenhagen, R., Turner, R. E., Yokota, R., and Khan, M. E · 2019
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Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift
Ovadia, Y., Fertig, E., Lakshminarayanan, B., Nowozin, S., Sculley, D., Dillon, J., Ren, J., Nado, Z., and Snoek, J · 2019
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Expressive priors in bayesian neural networks: Kernel combinations and periodic functions
Pearce, T., Tsuchida, R., Zaki, M., Brintrup, A., and Neely, A · 2019
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Bayesian batch active learning as sparse subset approximation
Pinsler, R., Gordon, J., Nalisnick, E., and Hernández-Lobato, J. M · 2019
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Functional variational bayesian neural networks
Sun, S., Zhang, G., Shi, J., and Grosse, R · 2019
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Depth Uncertainty in Neural Networks
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Overcoming catastrophic forgetting in neural networks
Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A. A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., Hassabis, D., Clopath, C., Kumaran, D., and Hadsell, R · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Lakshminarayanan, B., Pritzel, A., and Blundell, C · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Xiao, H., Rasul, K., and Vollgraf, R · 2017
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Fast and scalable bayesian deep learning by weight-perturbation in adam
Khan, M. E., Nielsen, D., Tangkaratt, V., Lin, W., Gal, Y., and Srivastava, A · 2018
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Slang: Fast structured covariance approximations for bayesian deep learning with natural gradient
Mishkin, A., Kunstner, F., Nielsen, D., Schmidt, M., and Khan, M. E · 2018
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Learning priors for invariance
Nalisnick, E. T. and Smyth, P · 2018
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Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty
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Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks
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New insights and perspectives on the natural gradient method
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The k-tied normal distribution: A compact parameterization of gaussian mean field posteriors in bayesian neural networks
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Picking winning tickets before training by preserving gradient flow
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Bayesian deep learning and a probabilistic perspective of generalization
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Laplace Redux–Effortless Bayesian Deep Learning
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Mixtures of Laplace Approximations for Improved Post-Hoc
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What are bayesian neural network posteriors really like?
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Learnable Uncertainty under Laplace Approximations
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Predictive complexity priors
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