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Paper Citation Record · LEDGER

Approximate Message Passing for Bayesian Neural Networks

As of 11 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2501.15573.

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

pith.paper-citation-record.v1
2501.15573 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T14:14:10.426487Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

36 of 36 outbound references displayed

  • verified exact8
  • verified fuzzy6
  • unresolved22
  • parse uncertain0
  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ccdf621b-4efc-49eb-9038-c8fc76855f33 · outbound

This paper cites Weight Uncertainty in Neural Networks.

Approximate Message Passing for Bayesian Neural Networks Weight Uncertainty in Neural Networks

Reference 1

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Observation 36435264-fac6-4fc9-bc28-e4cc62b85e6d · outbound

This paper cites End to End Learning for Self-Driving Cars.

Approximate Message Passing for Bayesian Neural Networks End to End Learning for Self-Driving Cars

Reference 2

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Observation 955cc1dd-5864-4811-a7c8-7f06071f212a · outbound

This paper cites Wide Mean-Field Bayesian Neural Networks Ignore the Data.

Approximate Message Passing for Bayesian Neural Networks Wide Mean-Field Bayesian Neural Networks Ignore the Data

Reference 3

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Observation 12a38ce9-2df4-473a-89a8-baa6302f50a3 · outbound

This paper cites Laplace Redux -- Effortless Bayesian Deep Learning.

Approximate Message Passing for Bayesian Neural Networks Laplace Redux -- Effortless Bayesian Deep Learning

Reference 4

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Observation 6cc686aa-6ef5-4452-89af-bd5542a13c27 · outbound

This paper cites Assumed density filtering methods for learning bayesian neural networks.

Approximate Message Passing for Bayesian Neural Networks Assumed density filtering methods for learning bayesian neural networks

Reference 5

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Observation e2d0e61d-707e-4f96-b18a-0b9bb2aea961 · outbound

This paper cites Practical variational inference for neural networks.

Approximate Message Passing for Bayesian Neural Networks Practical variational inference for neural networks

Reference 6

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Observation 6c978f17-c4c8-4499-9335-2a3c0349b85b · outbound

This paper cites Benchmarking uncertainty estimation methods for deep learning with safety-related metrics.

Approximate Message Passing for Bayesian Neural Networks Benchmarking uncertainty estimation methods for deep learning with safety-related metrics

Reference 7

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Observation 2b8a910d-3eb9-4e55-bbe3-16cad1721aee · outbound

This paper cites Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks.

Approximate Message Passing for Bayesian Neural Networks Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks

Reference 8

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Observation d5d9af88-ca2a-418a-8994-4d5311d873b1 · outbound

This paper cites Loopy belief propagation: Convergence and effects of message errors.

Approximate Message Passing for Bayesian Neural Networks Loopy belief propagation: Convergence and effects of message errors

Reference 9

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Observation f918146d-e3d2-48e0-b8c1-eb147d9eacfa · outbound

This paper cites The Bayesian Learning Rule.

Approximate Message Passing for Bayesian Neural Networks The Bayesian Learning Rule

Reference 10

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Observation 3ce2cf6f-adfb-4401-a587-c178ac8d9e4c · outbound

This paper cites Bayesian Dark Knowledge.

Approximate Message Passing for Bayesian Neural Networks Bayesian Dark Knowledge

Reference 11

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Observation 2125a081-b8c1-4bf3-9f53-13303ca05ae3 · outbound

This paper cites Kschischang, B.J.

Approximate Message Passing for Bayesian Neural Networks Kschischang, B.J

Reference 12

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Observation 68ca1d6f-a376-41de-8506-d72d14a50d59 · outbound

This paper cites On the detrimental effect of invariances in the likelihood for variational inference.

Approximate Message Passing for Bayesian Neural Networks On the detrimental effect of invariances in the likelihood for variational inference

Reference 13

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Observation 0faed823-d7ed-4061-bcb6-49f203227531 · outbound

This paper cites A ConvNet for the 2020s.

Approximate Message Passing for Bayesian Neural Networks A ConvNet for the 2020s

Reference 14

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Observation c0c5f1ba-7f3c-4395-96bf-ec22d84654b6 · outbound

This paper cites SGDR: Stochastic Gradient Descent with Warm Restarts.

Approximate Message Passing for Bayesian Neural Networks SGDR: Stochastic Gradient Descent with Warm Restarts

Reference 15

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Observation 7085f623-c57c-4ad9-90af-e8f30e731b70 · outbound

This paper cites Decoupled Weight Decay Regularization.

Approximate Message Passing for Bayesian Neural Networks Decoupled Weight Decay Regularization

Reference 16

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Observation 00ddb07d-9537-4e66-83f3-8c3799208bd7 · outbound

This paper cites Deep learning via message passing algorithms based on belief propagation.

Approximate Message Passing for Bayesian Neural Networks Deep learning via message passing algorithms based on belief propagation

Reference 17

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Observation f82d4cf4-242a-4027-a5d5-3df34e1851c3 · outbound

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Approximate Message Passing for Bayesian Neural Networks Unresolved cited work

Reference 18

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Observation 1878489f-23a8-4beb-a98b-aafcd131d7ee · outbound

This paper cites Eunnet: Efficient un-normalized convolution layer for stable training of deep residual networks without batch normalization layer.

Approximate Message Passing for Bayesian Neural Networks Eunnet: Efficient un-normalized convolution layer for stable training of deep residual networks without batch normalization layer

Reference 19

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Observation bf9b3da2-d0eb-4eec-8369-c0efc28c96d7 · outbound

This paper cites Practical Deep Learning with Bayesian Principles.

Approximate Message Passing for Bayesian Neural Networks Practical Deep Learning with Bayesian Principles

Reference 20

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Observation f7c74d0b-f0b2-4fd5-9bb6-03c82f758c07 · outbound

This paper cites Approximate blocked Gibbs sampling for Bayesian neural networks.

Approximate Message Passing for Bayesian Neural Networks Approximate blocked Gibbs sampling for Bayesian neural networks

Reference 21

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Observation a3519f90-f2f4-4003-8345-be9fecf2315f · outbound

This paper cites Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI.

Approximate Message Passing for Bayesian Neural Networks Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI

Reference 22

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Observation 05e0434f-218a-4261-84ce-32a27e4ef298 · outbound

This paper cites SAM 2: Segment Anything in Images and Videos.

Approximate Message Passing for Bayesian Neural Networks SAM 2: Segment Anything in Images and Videos

Reference 23

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Observation cccde684-5a3f-4819-b247-562f961edce9 · outbound

This paper cites Variational Learning is Effective for Large Deep Networks.

Approximate Message Passing for Bayesian Neural Networks Variational Learning is Effective for Large Deep Networks

Reference 24

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Observation 2fb408f7-c3d4-445f-925c-184c310530a5 · outbound

This paper cites Filter Response Normalization Layer: Eliminating Batch Dependence in the Training of Deep Neural Networks.

Approximate Message Passing for Bayesian Neural Networks Filter Response Normalization Layer: Eliminating Batch Dependence in the Training of Deep Neural Networks

Reference 25

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Observation 1ad29808-541a-499b-8f72-ad114c6d5b70 · outbound

This paper cites Expectation backpropagation: parameter-free training of multilayer neural networks with continuous or discrete weights.

Approximate Message Passing for Bayesian Neural Networks Expectation backpropagation: parameter-free training of multilayer neural networks with continuous or discrete weights

Reference 26

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Observation dfa4d09f-2422-403f-b016-f9071f6a9411 · outbound

This paper cites Matchbox: Large scale bayesian recommendations.

Approximate Message Passing for Bayesian Neural Networks Matchbox: Large scale bayesian recommendations

Reference 27

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Observation 686cd43e-a3d8-4133-b22e-b2cb5eac7ee2 · outbound

This paper cites Attention Is All You Need.

Approximate Message Passing for Bayesian Neural Networks Attention Is All You Need

Reference 28

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Observation 2cb9baf3-b721-4dda-bd33-a083a03220ae · outbound

This paper cites Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks.

Approximate Message Passing for Bayesian Neural Networks Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks

Reference 29

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Observation 849abfb7-ca28-4a51-a098-e6bd719f1342 · outbound

This paper cites A Spectral Condition for Feature Learning.

Approximate Message Passing for Bayesian Neural Networks A Spectral Condition for Feature Learning

Reference 30

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Observation df9a359e-bf46-4fe1-b3ba-bd6536bbe4cd · outbound

This paper cites Advances in Variational Inference.

Approximate Message Passing for Bayesian Neural Networks Advances in Variational Inference

Reference 31

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Observation 935d67b0-971e-487b-a558-948a640076d3 · outbound

This paper cites Beyond Recommendations: From Backward to Forward AI Support of Pilots' Decision-Making Process.

Approximate Message Passing for Bayesian Neural Networks Beyond Recommendations: From Backward to Forward AI Support of Pilots' Decision-Making Process

Reference 32

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Observation a113aa86-3728-4dd4-8e46-0e5f952d229e · outbound

This paper cites @esa (Ref.

Approximate Message Passing for Bayesian Neural Networks @esa (Ref

Reference 33

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Observation 7ffc499a-967e-4d71-b4cf-67c8a2a419b8 · outbound

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Approximate Message Passing for Bayesian Neural Networks Unresolved cited work

Reference 34

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Observation e0850478-691b-42aa-84dc-52c61f65e4da · outbound

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Approximate Message Passing for Bayesian Neural Networks , " * write output.state after.block = add.period write newline

Reference 35

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Observation 97ebbd7c-e1d3-4fb2-9e6d-44c6678b2dbe · outbound

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Approximate Message Passing for Bayesian Neural Networks write newline

Reference 36

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Pith citing papers

No inbound Pith citation observations are available.