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In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets.
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Stein variational gradient descent: A general purpose Bayesian inference algorithm
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Deep kernel learning
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Deep Bayesian active learning with image data
Gal, Y., Islam, R., and Ghahramani, Z · 2017
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Inconsistency of Bayesian inference for misspecified linear models, and a proposal for repairing it
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Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q · 2017
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Krueger, D., Huang, C.-W., Islam, R., Turner, R., Lacoste, A., and Courville, A · 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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Deep neural networks as Gaussian processes
Lee, J., Bahri, Y., Novak, R., Schoenholz, S. S., Pennington, J., and Sohl-Dickstein, J · 2017
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Bayesian compression for deep learning
Louizos, C., Ullrich, K., and Welling, M · 2017
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Stochastic gradient descent as approximate Bayesian inference
Mandt, S., Hoffman, M. D., and Blei, D. M · 2017
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Concrete problems for autonomous vehicle safety: Advantages of Bayesian deep learning
McAllister, R., Gal, Y., Kendall, A., Van Der Wilk, M., Shah, A., Cipolla, R., and Weller, A · 2017
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Variational dropout sparsifies deep neural networks
Molchanov, D., Ashukha, A., and Vetrov, D · 2017
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Structured Bayesian pruning via log-normal multiplicative noise
Neklyudov, K., Molchanov, D., Ashukha, A., and Vetrov, D. P · 2017
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Gaussian process behaviour in wide deep neural networks
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Structured variational learning of bayesian neural networks with horseshoe priors
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Neural tangent kernel: Convergence and generalization in neural networks
Jacot, A., Gabriel, F., and Hongler, C · 2018
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Probabilistic meta-representations of neural networks
Karaletsos, T., Dayan, P., and Ghahramani, Z · 2018
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Mixtures of g-priors in generalized linear models
Li, Y. and Clyde, M. A · 2018
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Posterior concentration for sparse deep learning
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A scalable Laplace approximation for neural networks
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A tutorial on Thompson sampling
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Bayesian deep learning for single-cell analysis
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Bayesian model-agnostic meta-learning
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Combining model and parameter uncertainty in Bayesian neural networks
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Successor uncertainties: exploration and uncertainty in temporal difference learning
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Approximate inference turns deep networks into Gaussian processes
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A simple baseline for Bayesian uncertainty in deep learning
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Galaxy merger rates up to z ∼ \sim 3 using a Bayesian deep learning model: A major-merger classifier using illustrisTNG simulation data
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