Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks | Paper Citation Record

Observation d7d5a995-c906-4129-b7ed-5cea953d3585 · outbound

This paper cites Huang, et al., Densely connected convolutional networks, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017) 2261–2269.

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Huang, et al., Densely connected convolutional networks, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017) 2261–2269

Reference 1

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This paper cites Zagoruyko, et al., Wide residual networks, in: E.

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Zagoruyko, et al., Wide residual networks, in: E

Reference 2

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This paper cites Mikolov, et al., Eﬃcient estimation of word representations in vector space, in: International Conference on Learning Representations, 2013.

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Mikolov, et al., Eﬃcient estimation of word representations in vector space, in: International Conference on Learning Representations, 2013

Reference 3

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Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Unresolved cited work

Reference 4

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This paper cites Vaswani, et al., Attention is all you need, in: I.

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Vaswani, et al., Attention is all you need, in: I

Reference 5

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Observation 9124dba4-1d81-49ba-a14b-4cb8c8d8555f · outbound

This paper cites Hinton, et al., Deep neural networks for acoustic modelling in speech recognition.

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Hinton, et al., Deep neural networks for acoustic modelling in speech recognition

Reference 6

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Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Unresolved cited work

Reference 7

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This paper cites Cohen, et al., Properties and beneﬁts of calibrated classiﬁers, in: Knowl- edge Discovery in Databases: PKDD 2004, Vol.

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Cohen, et al., Properties and beneﬁts of calibrated classiﬁers, in: Knowl- edge Discovery in Databases: PKDD 2004, Vol

Reference 8

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This paper cites Br¨ ummer, Measuring, reﬁning and calibrating speaker and language information extracted from speech, Ph.D.

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Br¨ ummer, Measuring, reﬁning and calibrating speaker and language information extracted from speech, Ph.D

Reference 9

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Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Unresolved cited work

Reference 10

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Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Unresolved cited work

Reference 11

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Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Unresolved cited work

Reference 12

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This paper cites Gulcehre, et al., On integrating a language model into neural machine translation, Comput.

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Gulcehre, et al., On integrating a language model into neural machine translation, Comput

Reference 13

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This paper cites Br¨ ummer, et al., On calibration of language recognition scores, in: Proc.

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Br¨ ummer, et al., On calibration of language recognition scores, in: Proc

Reference 14

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This paper cites Bojarski, et al., End to end learning for self-driving cars.

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Bojarski, et al., End to end learning for self-driving cars

Reference 15

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This paper cites Lee, et al., Training conﬁdence-calibrated classiﬁers for detecting out- of-distribution samples, in: International Conference On Learning Repre- sentations, 2018.

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Lee, et al., Training conﬁdence-calibrated classiﬁers for detecting out- of-distribution samples, in: International Conference On Learning Repre- sentations, 2018

Reference 16

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Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Unresolved cited work

Reference 17

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This paper cites Lakshminarayanan, et al., Simple and scalable predictive uncertainty estimation using deep ensembles, in: I.

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Lakshminarayanan, et al., Simple and scalable predictive uncertainty estimation using deep ensembles, in: I

Reference 18

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This paper cites Ramos, J.

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Ramos, J

Reference 19

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This paper cites Guo, et al., On calibration of modern neural networks, in: D.

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Guo, et al., On calibration of modern neural networks, in: D

Reference 20

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This paper cites Kuleshov, et al., Accurate uncertainties for deep learning using cali- brated regression, in: ICML, Vol.

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Kuleshov, et al., Accurate uncertainties for deep learning using cali- brated regression, in: ICML, Vol

Reference 21

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This paper cites Kumar, et al., Trainable calibration measures for neural networks from kernel mean embeddings, in: J.

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Kumar, et al., Trainable calibration measures for neural networks from kernel mean embeddings, in: J

Reference 22

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Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Unresolved cited work

Reference 23

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This paper cites Kendall, et al., What uncertainties do we need in bayesian deep learning for computer vision?, in: I.

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Kendall, et al., What uncertainties do we need in bayesian deep learning for computer vision?, in: I

Reference 24

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This paper cites Wu, et al., Fixing variational bayes: Deterministic variational inference for bayesian neural networks, in: International Conference On Learning Representations, 2019.

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Wu, et al., Fixing variational bayes: Deterministic variational inference for bayesian neural networks, in: International Conference On Learning Representations, 2019

Reference 25

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Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Unresolved cited work

Reference 26

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Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Unresolved cited work

Reference 27

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Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Unresolved cited work

Reference 28

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Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Unresolved cited work

Reference 29

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This paper cites Pereyra, et al., Regularizing neural networks by penalizing conﬁdent output distributions.

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Pereyra, et al., Regularizing neural networks by penalizing conﬁdent output distributions

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Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Unresolved cited work

Reference 31

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This paper cites DeVries, et al., Learning conﬁdence for out-of-distribution detection in neural networks.

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks DeVries, et al., Learning conﬁdence for out-of-distribution detection in neural networks

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Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Unresolved cited work

Reference 33

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Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Unresolved cited work

Reference 34

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This paper cites Louizos, et al., Multiplicative normalizing ﬂows for variational Bayesian neural networks, in: D.

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Louizos, et al., Multiplicative normalizing ﬂows for variational Bayesian neural networks, in: D

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This paper cites Dinh, et al., Density estimation using real nvp, in: International Con- ference on Learning Representations, 2017.

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Dinh, et al., Density estimation using real nvp, in: International Con- ference on Learning Representations, 2017

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Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Unresolved cited work

Reference 37

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Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Unresolved cited work

Reference 38

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This paper cites Ergodic Inference: Accelerate Convergence by Optimisation.

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Ergodic Inference: Accelerate Convergence by Optimisation

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Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Unresolved cited work

Reference 40

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Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Unresolved cited work

Reference 41

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This paper cites Gal, Uncertainty in deep learning, Ph.D.

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Gal, Uncertainty in deep learning, Ph.D

Reference 42

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This paper cites Snelson, et al., Sparse gaussian processes using pseudo-inputs, in: Y.

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Snelson, et al., Sparse gaussian processes using pseudo-inputs, in: Y

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This paper cites Havasi, et al., Inference in deep gaussian processes using stochastic gra- dient hamiltonian monte carlo, in: S.

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Havasi, et al., Inference in deep gaussian processes using stochastic gra- dient hamiltonian monte carlo, in: S

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Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Unresolved cited work

Reference 45

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This paper cites A Conceptual Introduction to Hamiltonian Monte Carlo.

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks A Conceptual Introduction to Hamiltonian Monte Carlo

Reference 46

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Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Unresolved cited work

Reference 47

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Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Unresolved cited work

Reference 48

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This paper cites Wah, et al., The Caltech-UCSD Birds-200-2011 Dataset, Tech.

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Wah, et al., The Caltech-UCSD Birds-200-2011 Dataset, Tech

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This paper cites Krause, M.

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Krause, M

Reference 50

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Observation 339d1738-f0f8-452a-96a7-329925e11ce6 · outbound

This paper cites Krizhevsky, et al., Cifar-100 (canadian institute for advanced research).

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Krizhevsky, et al., Cifar-100 (canadian institute for advanced research)

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Observation 69b55487-a8c9-4c20-86f9-45ba90aa65f7 · outbound

This paper cites Krizhevsky, et al., Cifar-10 (canadian institute for advanced research).

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Krizhevsky, et al., Cifar-10 (canadian institute for advanced research)

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:28:37.070308Z

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Observation 4a7c1f6a-69be-414a-8621-b2689bb82822 · outbound

This paper cites an unresolved cited work.

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Unresolved cited work

Reference 53

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This paper cites Cao, others., Vggface2: A dataset for recognising faces across pose and age, in: International Conference on Automatic Face and Gesture Recog- nition, 2018.

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Cao, others., Vggface2: A dataset for recognising faces across pose and age, in: International Conference on Automatic Face and Gesture Recog- nition, 2018

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:28:37.046426Z

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Observation 6cd6df20-bbd8-454d-879c-936403c0e998 · outbound

This paper cites Eidinger, et al., Age and gender estimation of unﬁltered faces, Trans.

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Eidinger, et al., Age and gender estimation of unﬁltered faces, Trans

Reference 55

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This paper cites Simonyan, et al., Very deep convolutional networks for large-scale im- age recognition, in: International Conference On Learning Representations, 2015.

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Simonyan, et al., Very deep convolutional networks for large-scale im- age recognition, in: International Conference On Learning Representations, 2015

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:28:37.033341Z

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Observation dff6b2e0-6c92-40d7-936f-437a00fefae3 · outbound

This paper cites He, et al., Deep residual learning for image recognition, in: 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp.

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks He, et al., Deep residual learning for image recognition, in: 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:28:36.988960Z

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Observation afecf95b-3555-4caf-a056-b5bf68c44ecf · outbound

This paper cites He, et al., Identity mappings in deep residual networks, in: ECCV, 2016.

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks He, et al., Identity mappings in deep residual networks, in: ECCV, 2016

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:28:36.909660Z

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This paper cites Chen, et al., Dual path networks, in: I.

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Chen, et al., Dual path networks, in: I

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:28:36.860769Z

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Observation b920de71-dd4b-46d3-bb0c-cfd4e2317820 · outbound

This paper cites Xie, et al., Aggregated residual transformations for deep neural net- works, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017) 5987–5995.

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Xie, et al., Aggregated residual transformations for deep neural net- works, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017) 5987–5995

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:28:36.848406Z

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Observation efb3a581-3109-4a99-934c-d883f75efa0f · outbound

This paper cites Sandler, et al., Mobilenetv2: Inverted residuals and linear bottlenecks, in: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Sandler, et al., Mobilenetv2: Inverted residuals and linear bottlenecks, in: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:28:36.836370Z

Source-reported events for the cited work

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Observation 4c1240fa-e137-4adb-84b8-9789f4bacaba · outbound

This paper cites Hu, et al., Squeeze-and-excitation networks, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (2018) 7132–7141.

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Hu, et al., Squeeze-and-excitation networks, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (2018) 7132–7141

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:28:36.825094Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a3f5527c-710d-43d9-8fd0-ed6768f533ba · outbound

This paper cites an unresolved cited work.

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Unresolved cited work

Reference 63

Resolution: unresolved; raw_fallback, observed 2026-08-14T11:28:36.804814Z

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Observation 4f894078-f21a-47ec-871d-63366975b95c · outbound

This paper cites Goodfellow, et al., Explaining and harnessing adversarial examples, in: International Conference on Learning Representations, 2015.

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Goodfellow, et al., Explaining and harnessing adversarial examples, in: International Conference on Learning Representations, 2015

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:28:36.657061Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 94da322d-9ee4-4b5d-bfe7-9c6a6c458fe4 · outbound

This paper cites Gal, et al., Concrete dropout, in: I.

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Gal, et al., Concrete dropout, in: I

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:28:36.556941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4082ad92-934d-4ecb-bfe0-cb6d316b7355 · outbound

This paper cites Tran, et al., Calibrating deep convolutional gaussian processes, in: K.

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Tran, et al., Calibrating deep convolutional gaussian processes, in: K

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:28:36.543391Z

Source-reported events for the cited work

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Observation ae368e31-8456-4a06-b0e7-a1051a8ebbf9 · outbound

This paper cites Milios, othes, Dirichlet-based gaussian processes for large-scale cali- brated classiﬁcation, in: S.

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Milios, othes, Dirichlet-based gaussian processes for large-scale cali- brated classiﬁcation, in: S

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:28:36.530723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 7df9baad-db54-45ed-9095-f771ba22abbd · outbound

This paper cites an unresolved cited work.

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Unresolved cited work

Reference 68

Resolution: unresolved; raw_fallback, observed 2026-08-14T11:28:36.518855Z

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Observation fd047f2c-a749-4586-895a-c0b0cb27527e · outbound

This paper cites Huang, et al., Neural autoregressive ﬂows, in: ICML, 2018.

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Huang, et al., Neural autoregressive ﬂows, in: ICML, 2018

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:28:36.491208Z

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Observation 603458ec-a5ab-45aa-aec9-fd6cbd663965 · outbound

This paper cites Globerson, A.

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Globerson, A

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:28:36.376716Z

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Observation 7ea03189-e785-472a-8935-81fe0b3f9c57 · outbound

This paper cites an unresolved cited work.

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Unresolved cited work

Reference 71

Resolution: unresolved; raw_fallback, observed 2026-08-14T11:28:36.322363Z

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Observation f1f5297d-8e44-4230-af52-fd5180807fc5 · outbound

This paper cites Ranganath, et al., Hierarchical variational models, in: M.

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Ranganath, et al., Hierarchical variational models, in: M

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:28:36.309713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0c01b5fb-aed7-44a7-b612-fc87d6f45250 · outbound

This paper cites Cremer, et al., Inference suboptimality in variational autoencoders, in: J.

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Cremer, et al., Inference suboptimality in variational autoencoders, in: J

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:28:36.297498Z

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Observation 48cf3704-c76e-40e5-bca8-0483cbffec15 · outbound

This paper cites Shu, et al., Amortized inference regularization, in: S.

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Shu, et al., Amortized inference regularization, in: S

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:28:36.284455Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c59a67bc-795b-48e1-b45e-c300981bb226 · outbound

This paper cites Kim, et al., Semi-amortized variational autoencoders, in: J.

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Kim, et al., Semi-amortized variational autoencoders, in: J

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:28:36.271556Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9e167099-7962-4818-ab8d-523bfc948772 · outbound

This paper cites Generalized Variational Inference: Three arguments for deriving new Posteriors.

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Generalized Variational Inference: Three arguments for deriving new Posteriors

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-14T11:28:35.839586Z

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Observation 510b1c93-070d-4978-990b-9bfe1fbd3d66 · outbound

This paper cites an unresolved cited work.

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Unresolved cited work

Reference 778

Resolution: unresolved; no resolver link, observed 2026-08-14T11:28:35.529930Z

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