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Bayesian neural networks (BNNs) have recently gained popularity due to their ability to quantify model uncertainty.
Bayesian Methods for Adaptive Models
David John Cameron MacKay · 1992
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Probable Networks and Plausible Predictions — A Review of Practical Bayesian Methods for Supervised Neural Networks
David J C MacKay · 1995
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Bayesian Learning for Neural Networks
Radford M. Neal · 1996
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The MNIST Database of Handwritten Digits, 1998
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Bayesian Learning via Stochastic Gradient Langevin Dynamics
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A Kernel Two-Sample Test
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Stochastic Gradient Hamiltonian Monte Carlo
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Equality of Opportunity in Supervised Learning
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Variational Inference: A Review for Statisticians
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The Prior Can Often Only Be Understood in the Context of the Likelihood
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Inconsistency of Bayesian Inference for Misspecified Linear Models, and a Proposal for Repairing It
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Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
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Laplace Redux: Effortless Bayesian Deep Learning
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Shengyang Sun, Changyou Chen, and Lawrence Carin · 2017
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Controlling Neural Networks with Rule Representations
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Priors in Bayesian Deep Learning: A Review
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Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors
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A Kernel Two-Sample Test for Functional Data
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Posterior Regularized Bayesian Neural Network Incorporating Soft and Hard Knowledge Constraints
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MIMIC-IV, A Freely Accessible Electronic Health Record Dataset
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Learning with Explanation Constraints
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Incorporating Unlabelled Data into Bayesian Neural Networks
Mrinank Sharma, Tom Rainforth, Yee Whye Teh, and Vincent Fortuin · 2023
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