Paper Citation Record · LEDGER

Streamlining Prediction in Bayesian Deep Learning

As of 13 August 2026, this Paper Citation Record lists 72 of 72 outbound references and 1 inbound Pith citation observation for arXiv:2411.18425.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2411.18425 v4

Coverage vector

measured 72 of 72 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T11:16:07.861480Z

measured 73 of 73 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-26T08:45:34.884703Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

72 of 72 outbound references displayed

verified exact2
verified fuzzy56
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External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 12233be5-34b2-4d13-aaa2-8fc991c50ca9 · outbound

This paper cites Post-hoc probabilistic vision-language models.

Streamlining Prediction in Bayesian Deep Learning Post-hoc probabilistic vision-language models

Reference 1

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Observation 1990a506-776d-4714-ab86-74b9f93f1412 · outbound

This paper cites The need for uncertainty quantification in machine-assisted medical decision making.

Streamlining Prediction in Bayesian Deep Learning The need for uncertainty quantification in machine-assisted medical decision making

Reference 2

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Observation bdad991c-13e4-4637-99de-7580d2339da7 · outbound

This paper cites Variational inference: A review for statisticians.

Streamlining Prediction in Bayesian Deep Learning Variational inference: A review for statisticians

Reference 3

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Observation 15bc754f-d2a0-4a9c-9c9c-e9dae8115326 · outbound

This paper cites Weight uncertainty in neural network.

Streamlining Prediction in Bayesian Deep Learning Weight uncertainty in neural network

Reference 4

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Observation b35fbc8a-1eb6-4133-bdff-d2b2de4f6c2b · outbound

This paper cites Sample average approximation for black-box variational inference.

Streamlining Prediction in Bayesian Deep Learning Sample average approximation for black-box variational inference

Reference 5

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Observation e779d96e-8ed3-47c7-bb71-d9a2dffd16ce · outbound

This paper cites Remote sensing image scene classification: Benchmark and state of the art.

Streamlining Prediction in Bayesian Deep Learning Remote sensing image scene classification: Benchmark and state of the art

Reference 6

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Observation 7855537c-ec5b-4a10-8c62-122ff54230be · outbound

This paper cites Describing textures in the wild.

Streamlining Prediction in Bayesian Deep Learning Describing textures in the wild

Reference 7

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Observation fce4c868-49df-490b-a5a4-6f08f83123b3 · outbound

This paper cites Wide mean-field bayesian neural networks ignore the data.

Streamlining Prediction in Bayesian Deep Learning Wide mean-field bayesian neural networks ignore the data

Reference 8

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Observation 37e837a5-9257-4791-af59-3516be01b2e5 · outbound

This paper cites Kronecker-factored approximate curvature (kfac) from scratch.

Streamlining Prediction in Bayesian Deep Learning Kronecker-factored approximate curvature (kfac) from scratch

Reference 9

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Observation 9f18fb92-7e2a-46f6-a5e3-459f4fdb81f9 · outbound

This paper cites Laplace redux -- effortless B ayesian deep learning.

Streamlining Prediction in Bayesian Deep Learning Laplace redux -- effortless B ayesian deep learning

Reference 10

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Observation 9e73eccd-33ed-4810-9071-2625770c6b09 · outbound

This paper cites B ayesian deep learning via subnetwork inference.

Streamlining Prediction in Bayesian Deep Learning B ayesian deep learning via subnetwork inference

Reference 11

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Observation e9f2fdef-e023-49f5-b108-c1c4deec0df9 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Streamlining Prediction in Bayesian Deep Learning Imagenet: A large-scale hierarchical image database

Reference 12

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Observation 306ed092-7c6c-48ad-a58b-4b8700f2b94e · outbound

This paper cites Efficient parametric approximations of neural network function space distance.

Streamlining Prediction in Bayesian Deep Learning Efficient parametric approximations of neural network function space distance

Reference 13

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Observation 52dde7b4-b559-4273-8c85-dba703d17f2e · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Streamlining Prediction in Bayesian Deep Learning An image is worth 16x16 words: Transformers for image recognition at scale

Reference 14

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Observation 78e31b69-44ad-45cb-906a-6843b3b4594c · outbound

This paper cites Mixtures of L apkace approximations for improved post-hoc uncertainty in deep learning.

Streamlining Prediction in Bayesian Deep Learning Mixtures of L apkace approximations for improved post-hoc uncertainty in deep learning

Reference 15

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Observation d9bfb01e-f154-4314-b27f-074c53d1b8e7 · outbound

This paper cites On the expressiveness of approximate inference in B ayesian neural networks.

Streamlining Prediction in Bayesian Deep Learning On the expressiveness of approximate inference in B ayesian neural networks

Reference 16

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Observation 37785a43-ea46-43b0-a666-3fb8e50aa4da · outbound

This paper cites B ayesian neural network priors revisited.

Streamlining Prediction in Bayesian Deep Learning B ayesian neural network priors revisited

Reference 17

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Observation f759a4c2-9a0a-4f65-8e96-6a79f39c5ac5 · outbound

This paper cites Dropout as a bayesian approximation: Representing model uncertainty in deep learning.

Streamlining Prediction in Bayesian Deep Learning Dropout as a bayesian approximation: Representing model uncertainty in deep learning

Reference 18

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Observation 3c7250cd-147a-4a87-af29-41b59ab80ebd · outbound

This paper cites Deep bayesian active learning with image data.

Streamlining Prediction in Bayesian Deep Learning Deep bayesian active learning with image data

Reference 19

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Streamlining Prediction in Bayesian Deep Learning Unresolved cited work

Reference 20

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Observation eb78e3d0-e17d-4cb2-9097-e3c46aa6af6f · outbound

This paper cites Black box variational inference with a deterministic objective: Faster, more accurate, and even more black box.

Streamlining Prediction in Bayesian Deep Learning Black box variational inference with a deterministic objective: Faster, more accurate, and even more black box

Reference 21

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Observation d07181b6-4680-4bb4-8ae4-9e25778edfc2 · outbound

This paper cites Tractable approximate G aussian inference for B ayesian neural networks.

Streamlining Prediction in Bayesian Deep Learning Tractable approximate G aussian inference for B ayesian neural networks

Reference 22

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Observation 353a7634-9d6b-40ea-94a8-bfd479b24093 · outbound

This paper cites Training independent subnetworks for robust prediction.

Streamlining Prediction in Bayesian Deep Learning Training independent subnetworks for robust prediction

Reference 23

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Observation a5b01e40-2c33-4626-8dd6-921c19a22b16 · outbound

This paper cites Deep residual learning for image recognition.

Streamlining Prediction in Bayesian Deep Learning Deep residual learning for image recognition

Reference 24

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Observation 452f3cac-db79-4e0e-818c-db57c754c481 · outbound

This paper cites The many faces of robustness: A critical analysis of out-of-distribution generalization.

Streamlining Prediction in Bayesian Deep Learning The many faces of robustness: A critical analysis of out-of-distribution generalization

Reference 25

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Observation 60777dae-9b2d-4c30-b65f-1547367dbd8c · outbound

This paper cites Scalable marginal likelihood estimation for model selection in deep learning.

Streamlining Prediction in Bayesian Deep Learning Scalable marginal likelihood estimation for model selection in deep learning

Reference 26

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Observation 09c70b19-1669-4566-911d-121e5d559957 · outbound

This paper cites Improving predictions of B ayesian neural nets via local linearization.

Streamlining Prediction in Bayesian Deep Learning Improving predictions of B ayesian neural nets via local linearization

Reference 27

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Observation 65d2e54f-5a05-4fd8-9e0f-9bc971116490 · outbound

This paper cites Towards scalable B ayesian transformers: Investigating stochastic subset selection for nlp.

Streamlining Prediction in Bayesian Deep Learning Towards scalable B ayesian transformers: Investigating stochastic subset selection for nlp

Reference 28

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Observation 353e1950-3981-4a30-a692-0d1399a56a5b · outbound

This paper cites From moments of sum to moments of product.

Streamlining Prediction in Bayesian Deep Learning From moments of sum to moments of product

Reference 29

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Observation 1b0549f0-b291-46cb-9d0b-cf3fb5d6d359 · outbound

This paper cites The UCI machine learning repository, 2023.

Streamlining Prediction in Bayesian Deep Learning The UCI machine learning repository, 2023

Reference 30

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Observation e6330644-3785-463f-8ec1-0e429db9dd93 · outbound

This paper cites Being bayesian, even just a bit, fixes overconfidence in relu networks.

Streamlining Prediction in Bayesian Deep Learning Being bayesian, even just a bit, fixes overconfidence in relu networks

Reference 31

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Observation b1bddcfc-81ce-4787-9b5a-4228fa332239 · outbound

This paper cites Promises and pitfalls of the linearized L apkace in B ayesian optimization.

Streamlining Prediction in Bayesian Deep Learning Promises and pitfalls of the linearized L apkace in B ayesian optimization

Reference 32

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Observation 30c3a585-e993-46b1-a4b0-d91fcc86001e · outbound

This paper cites Learning multiple layers of features from tiny images.

Streamlining Prediction in Bayesian Deep Learning Learning multiple layers of features from tiny images

Reference 33

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Observation 0e5c3aaf-0d88-485a-b9e3-f86823f78bbe · outbound

This paper cites Simple and scalable predictive uncertainty estimation using deep ensembles.

Streamlining Prediction in Bayesian Deep Learning Simple and scalable predictive uncertainty estimation using deep ensembles

Reference 34

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This paper cites Gradient-based learning applied to document recognition.

Streamlining Prediction in Bayesian Deep Learning Gradient-based learning applied to document recognition

Reference 35

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This paper cites Soft: Softmax-free transformer with linear complexity.

Streamlining Prediction in Bayesian Deep Learning Soft: Softmax-free transformer with linear complexity

Reference 36

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Observation ce5bff86-e0ce-40b9-821c-be5251bef8b9 · outbound

This paper cites Information-based objective functions for active data selection.

Streamlining Prediction in Bayesian Deep Learning Information-based objective functions for active data selection

Reference 37

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This paper cites B ayesian interpolation.

Streamlining Prediction in Bayesian Deep Learning B ayesian interpolation

Reference 38

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Streamlining Prediction in Bayesian Deep Learning B ayesian methods for backpropagation networks

Reference 39

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This paper cites Maddox, Pavel Izmailov, Timur Garipov, Dmitry P.

Streamlining Prediction in Bayesian Deep Learning Maddox, Pavel Izmailov, Timur Garipov, Dmitry P

Reference 40

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This paper cites Optimizing neural networks with K ronecker-factored approximate curvature.

Streamlining Prediction in Bayesian Deep Learning Optimizing neural networks with K ronecker-factored approximate curvature

Reference 41

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Observation d5fb0ae5-1429-485d-bac2-d540cf4bb267 · outbound

This paper cites Periodic activation functions induce stationarity.

Streamlining Prediction in Bayesian Deep Learning Periodic activation functions induce stationarity

Reference 42

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Observation 07cf5c76-341e-44fc-a8e8-efdd9389f22a · outbound

This paper cites Fixing overconfidence in dynamic neural networks.

Streamlining Prediction in Bayesian Deep Learning Fixing overconfidence in dynamic neural networks

Reference 43

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Observation 06f7f882-32ba-4626-a20c-30e1816da56b · outbound

This paper cites Uncertainty quantification with statistical guarantees in end-to-end autonomous driving control.

Streamlining Prediction in Bayesian Deep Learning Uncertainty quantification with statistical guarantees in end-to-end autonomous driving control

Reference 44

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Streamlining Prediction in Bayesian Deep Learning On the distribution of the product of correlated normal random variables

Reference 45

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Observation 639d4725-cf7d-4e53-9dfe-5b1d1de1d4b7 · outbound

This paper cites On priors for B ayesian neural networks.

Streamlining Prediction in Bayesian Deep Learning On priors for B ayesian neural networks

Reference 46

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Observation 67c1685b-e045-4a79-ad5b-f63273cabd2c · outbound

This paper cites Reading digits in natural images with unsupervised feature learning.

Streamlining Prediction in Bayesian Deep Learning Reading digits in natural images with unsupervised feature learning

Reference 47

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Observation c9bd1d38-2ec5-4e1b-a9af-adc21134c300 · outbound

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Streamlining Prediction in Bayesian Deep Learning Unresolved cited work

Reference 48

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Observation bbf75867-c169-4c61-a22e-e662573a557d · outbound

This paper cites Uncertainty quantification via stable distribution propagation.

Streamlining Prediction in Bayesian Deep Learning Uncertainty quantification via stable distribution propagation

Reference 49

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This paper cites Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons.

Streamlining Prediction in Bayesian Deep Learning Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons

Reference 50

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This paper cites Language models are unsupervised multitask learners.

Streamlining Prediction in Bayesian Deep Learning Language models are unsupervised multitask learners

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:16:08.180987Z

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Observation fea3f811-0b46-4618-b263-bf03e355c9ad · outbound

This paper cites A scalable L aplace approximation for neural networks.

Streamlining Prediction in Bayesian Deep Learning A scalable L aplace approximation for neural networks

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:16:08.168598Z

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Observation a716494f-9263-434c-a4f6-e9529fb7abf5 · outbound

This paper cites B ayesian Filtering and Smoothing.

Streamlining Prediction in Bayesian Deep Learning B ayesian Filtering and Smoothing

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:16:08.157996Z

Source-reported events for the cited work

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Observation 0e2453f0-4852-436b-8981-26c57144f62e · outbound

This paper cites Function-space parameterization of neural networks for sequential learning.

Streamlining Prediction in Bayesian Deep Learning Function-space parameterization of neural networks for sequential learning

Reference 54

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Observation 3aedffae-5d73-495c-a870-ab26f92295a2 · outbound

This paper cites Variational learning is effective for large deep networks.

Streamlining Prediction in Bayesian Deep Learning Variational learning is effective for large deep networks

Reference 55

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Observation 6b95179b-9d73-4c20-8628-0132f41aca3e · outbound

This paper cites Prediction-oriented bayesian active learning.

Streamlining Prediction in Bayesian Deep Learning Prediction-oriented bayesian active learning

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:16:08.125032Z

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Observation c01e68a5-9658-44cc-8f69-f4ea711b9137 · outbound

This paper cites All you need is a good functional prior for B ayesian deep learning.

Streamlining Prediction in Bayesian Deep Learning All you need is a good functional prior for B ayesian deep learning

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:16:08.114099Z

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Observation f4a27e6f-9998-4950-a535-ada52e716c72 · outbound

This paper cites Attention is all you need.

Streamlining Prediction in Bayesian Deep Learning Attention is all you need

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:16:08.101827Z

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Observation cf633de5-b5f0-42b9-801f-b3284881f6c0 · outbound

This paper cites High-dimensional G aussian sampling: a review and a unifying approach based on a stochastic proximal point algorithm.

Streamlining Prediction in Bayesian Deep Learning High-dimensional G aussian sampling: a review and a unifying approach based on a stochastic proximal point algorithm

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:16:08.090158Z

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Observation b79b4f62-4481-49f2-bace-0ab20f18f80d · outbound

This paper cites Superglue: A stickier benchmark for general-purpose language understanding systems.

Streamlining Prediction in Bayesian Deep Learning Superglue: A stickier benchmark for general-purpose language understanding systems

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:16:08.078678Z

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Observation dbd0daf9-d3a9-417e-892e-9b6a2ec8e5a8 · outbound

This paper cites Glue: A multi-task benchmark and analysis platform for natural language understanding.

Streamlining Prediction in Bayesian Deep Learning Glue: A multi-task benchmark and analysis platform for natural language understanding

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:16:08.065915Z

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Observation 78bf5183-35ce-40c3-a568-37ce1a8f28e1 · outbound

This paper cites an unresolved cited work.

Streamlining Prediction in Bayesian Deep Learning Unresolved cited work

Reference 62

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Observation a928a52d-7fb8-4d8b-be5e-22ba9b889330 · outbound

This paper cites The Case for Bayesian Deep Learning.

Streamlining Prediction in Bayesian Deep Learning The Case for Bayesian Deep Learning

Reference 63

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Observation b071c43a-4c1a-4433-a3bd-9f0aecf45602 · outbound

This paper cites B ayesian deep learning and a probabilistic perspective of generalization.

Streamlining Prediction in Bayesian Deep Learning B ayesian deep learning and a probabilistic perspective of generalization

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:16:08.038942Z

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Observation 1058be98-742b-441b-af98-7d8deb7ce7f6 · outbound

This paper cites HuggingFace's Transformers: State-of-the-art Natural Language Processing.

Streamlining Prediction in Bayesian Deep Learning HuggingFace's Transformers: State-of-the-art Natural Language Processing

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-12T11:16:07.832395Z

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Observation 41a27bf8-48e7-4803-b8dc-0f74f5491cd6 · outbound

This paper cites Gaussian Pre-Activations in Neural Networks: Myth or Reality?.

Streamlining Prediction in Bayesian Deep Learning Gaussian Pre-Activations in Neural Networks: Myth or Reality?

Reference 66

Resolution: verified exact; local_arxiv, observed 2026-08-12T11:16:07.919420Z

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Observation ee6ee670-f19e-449d-8fc3-1d8e8a4f70e5 · outbound

This paper cites Turner, Jos \' e Miguel Hern \' a ndez - Lobato, and Alexander L.

Streamlining Prediction in Bayesian Deep Learning Turner, Jos \' e Miguel Hern \' a ndez - Lobato, and Alexander L

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:16:08.026647Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 9696c489-f21b-41b2-81f6-e403767902c4 · outbound

This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

Streamlining Prediction in Bayesian Deep Learning Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-12T11:16:07.844903Z

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Observation 6ffc4205-2e1e-4b6d-bb57-851145eaa553 · outbound

This paper cites B ayesian low-rank adaptation for large language models.

Streamlining Prediction in Bayesian Deep Learning B ayesian low-rank adaptation for large language models

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:16:08.013838Z

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Observation 3901859e-c9ff-4013-97c9-56807c13287a · outbound

This paper cites Rubin, and Holger R.

Streamlining Prediction in Bayesian Deep Learning Rubin, and Holger R

Reference 70

Resolution: verified exact; doi, observed 2026-08-12T11:16:07.891699Z

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Observation 0027b04f-863e-44b7-8ef3-57f1c7eaa1a3 · outbound

This paper cites u tepage, Hedvig Kjellstr \.

Streamlining Prediction in Bayesian Deep Learning u tepage, Hedvig Kjellstr \

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:16:07.995070Z

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Observation 526957b5-fe5b-4705-8304-8a68c63a9ad6 · outbound

This paper cites write newline.

Streamlining Prediction in Bayesian Deep Learning write newline

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-12T11:16:07.861480Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 4912675a-5d68-4d04-9f65-955547103d60 · inbound

Quantifying the Agreement Between Data-Influence and Data-Similarity to Understand LLM Behavior cites this paper.

Quantifying the Agreement Between Data-Influence and Data-Similarity to Understand LLM Behavior Streamlining Prediction in Bayesian Deep Learning

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-06-26T08:49:14.839196Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.