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2Deep Orientation Uncertainty Learning based on a Bingham Loss
I. Gilitschenski, R. Sahoo, W. Schwarting, A. Amini, S. Karaman, and D. Rus. International Conference on Learning Representations, (2020)
5 years ago by @kirk86
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2Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors
M. Dusenberry, G. Jerfel, Y. Wen, Y. Ma, J. Snoek, K. Heller, B. Lakshminarayanan, and D. Tran. (2020)cite arxiv:2005.07186Comment: Code available at https://github.com/google/edward2.
5 years ago by @kirk86
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1Uncertainty Quantification for Data-driven Turbulence Modelling with Mondrian Forests
A. Scillitoe, P. Seshadri, and M. Girolami. (2020)cite arxiv:2003.01968Comment: 24 pages, 21 figures, submitted to Journal of Computational Physics.
5 years ago by @kirk86
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3Bayesian Deep Learning and a Probabilistic Perspective of Generalization
A. Wilson, and P. Izmailov. (2020)cite arxiv:2002.08791Comment: 27 pages, 17 figures.
5 years ago by @kirk86
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1Constructing Priors that Penalize the Complexity of Gaussian Random Fields
G. Fuglstad, D. Simpson, F. Lindgren, and H. Rue. (2015)cite arxiv:1503.00256.
5 years ago by @kirk86
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3Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights
T. Karaletsos, and T. Bui. (2020)cite arxiv:2002.04033Comment: 12 pages main paper, 13 pages appendix.
5 years ago by @kirk86
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1'In-Between' Uncertainty in Bayesian Neural Networks
A. Foong, Y. Li, J. Hernández-Lobato, and R. Turner. (2019)cite arxiv:1906.11537Comment: Presented at the ICML 2019 Workshop on Uncertainty and Robustness in Deep Learning.
5 years ago by @kirk86
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2A mean-field theory of lazy training in two-layer neural nets: entropic regularization and controlled McKean-Vlasov dynamics
B. Tzen, and M. Raginsky. (2020)cite arxiv:2002.01987.
5 years ago by @kirk86
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1How Good is the Bayes Posterior in Deep Neural Networks Really?
F. Wenzel, K. Roth, B. Veeling, J. Świątkowski, L. Tran, S. Mandt, J. Snoek, T. Salimans, R. Jenatton, and S. Nowozin. (2020)cite arxiv:2002.02405.
5 years ago by @kirk86
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4Practical Deep Learning with Bayesian Principles
K. Osawa, S. Swaroop, A. Jain, R. Eschenhagen, R. Turner, R. Yokota, and M. Khan. (2019)cite arxiv:1906.02506Comment: NeurIPS 2019.
5 years ago by @kirk86
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2Bayesian Variational Autoencoders for Unsupervised Out-of-Distribution Detection
E. Daxberger, and J. Hernández-Lobato. (2019)cite arxiv:1912.05651Comment: 18 pages, extended version with supplementary material.
5 years ago by @kirk86
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2Towards neural networks that provably know when they don't know
A. Meinke, and M. Hein. International Conference on Learning Representations, (2020)
5 years ago by @kirk86
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1Most Probable Explanations In Bayesian Networks: Complexity And Tractability
J. Kwisthout. (2011)
5 years ago by @kirk86
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1Fast and accurate approximation of the full conditional for gamma shape parameters
J. Miller. (2018)cite arxiv:1802.01610.
5 years ago by @kirk86
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2Quantile Propagation for Wasserstein-Approximate Gaussian Processes
R. Zhang, C. Walder, E. Bonilla, M. Rizoiu, and L. Xie. (2019)cite arxiv:1912.10200.
5 years ago by @kirk86
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1Robust Bayesian inference via coarsening
J. Miller, and D. Dunson. (2015)cite arxiv:1506.06101.
5 years ago by @kirk86
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1Generalized Variational Inference: Three arguments for deriving new Posteriors
J. Knoblauch, J. Jewson, and T. Damoulas. (2019)cite arxiv:1904.02063Comment: 103 pages, 23 figures (comprehensive revision of previous version).
5 years ago by @kirk86
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1Stochastic Variational Optimization
T. Bird, J. Kunze, and D. Barber. (2018)cite arxiv:1809.04855.
5 years ago by @kirk86
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2Input complexity and out-of-distribution detection with likelihood-based generative models
J. Serrà, D. Álvarez, V. Gómez, O. Slizovskaia, J. Núñez, and J. Luque. (2019)cite arxiv:1909.11480Comment: Includes appendix.
5 years ago by @kirk86
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2Don't Blame the ELBO! A Linear VAE Perspective on Posterior Collapse
J. Lucas, G. Tucker, R. Grosse, and M. Norouzi. (2019)cite arxiv:1911.02469Comment: 11 main pages, 10 appendix pages. 13 figures total. Accepted at 33rd Conference on Neural Information Processing Systems (NeurIPS 2019).
5 years ago by @kirk86
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publications (hide)25
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2Deep Orientation Uncertainty Learning based on a Bingham Loss

2Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors

1Uncertainty Quantification for Data-driven Turbulence Modelling with Mondrian Forests

3Bayesian Deep Learning and a Probabilistic Perspective of Generalization

1Constructing Priors that Penalize the Complexity of Gaussian Random Fields

3Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights

1'In-Between' Uncertainty in Bayesian Neural Networks

2A mean-field theory of lazy training in two-layer neural nets: entropic regularization and controlled McKean-Vlasov dynamics

1How Good is the Bayes Posterior in Deep Neural Networks Really?

4Practical Deep Learning with Bayesian Principles

2Bayesian Variational Autoencoders for Unsupervised Out-of-Distribution Detection

2Towards neural networks that provably know when they don't know

1Most Probable Explanations In Bayesian Networks: Complexity And Tractability

1Fast and accurate approximation of the full conditional for gamma shape parameters

2Quantile Propagation for Wasserstein-Approximate Gaussian Processes

1Robust Bayesian inference via coarsening

1Generalized Variational Inference: Three arguments for deriving new Posteriors

1Stochastic Variational Optimization

2Input complexity and out-of-distribution detection with likelihood-based generative models

2Don't Blame the ELBO! A Linear VAE Perspective on Posterior Collapse

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