Paper Citation Record · LEDGER

Last-layer committee machines for uncertainty estimations of benthic imagery

As of 19 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 1 inbound Pith citation observation for arXiv:2504.16952.

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

pith.paper-citation-record.v1
2504.16952 v2

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:14:56.678893Z

measured 51 of 51 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T03:09:19.673622Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

50 of 50 outbound references displayed

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verified fuzzy7
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External citation measurements

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Outbound references

Observation 2ce7ed4b-c5a3-4295-83b5-a278c6ccdfed · outbound

This paper cites , author Pourpanah, F.

Last-layer committee machines for uncertainty estimations of benthic imagery , author Pourpanah, F

Reference 1

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Observation ac6e250d-92ec-4a8a-ba72-07e7bc17f41e · outbound

This paper cites , author Hill, N.

Last-layer committee machines for uncertainty estimations of benthic imagery , author Hill, N

Reference 2

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Observation a610b7a8-029a-4671-932d-e5d7a829a6c7 · outbound

This paper cites , author Hill, N.

Last-layer committee machines for uncertainty estimations of benthic imagery , author Hill, N

Reference 3

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Observation 21987ec0-091c-4a42-8b9e-b376672152f0 · outbound

This paper cites A Primer on Bayesian Neural Networks: Review and Debates.

Last-layer committee machines for uncertainty estimations of benthic imagery A Primer on Bayesian Neural Networks: Review and Debates

Reference 4

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This paper cites , author Hobley, B.

Last-layer committee machines for uncertainty estimations of benthic imagery , author Hobley, B

Reference 5

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Observation f73ad196-5898-4acb-86c3-3a1657eedc93 · outbound

This paper cites , author Treibitz, T.

Last-layer committee machines for uncertainty estimations of benthic imagery , author Treibitz, T

Reference 6

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Observation 9c35439c-8c99-482c-99c5-c997a91f4f99 · outbound

This paper cites , author Chen, J.P.

Last-layer committee machines for uncertainty estimations of benthic imagery , author Chen, J.P

Reference 7

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Observation 3b2c33ff-8b53-4aef-ab9e-7328ef3abeee · outbound

This paper cites , author Sameoto, J.A.

Last-layer committee machines for uncertainty estimations of benthic imagery , author Sameoto, J.A

Reference 8

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Observation 6564cfc1-60f6-41aa-823d-0a4495042c7f · outbound

This paper cites , author Smith, S.J.

Last-layer committee machines for uncertainty estimations of benthic imagery , author Smith, S.J

Reference 9

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Observation 70acb806-389d-4e7e-8c43-8b8cf68d4001 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Last-layer committee machines for uncertainty estimations of benthic imagery An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 10

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Observation d34dfd2b-e457-4ca1-ab30-5c57961148b0 · outbound

This paper cites AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification.

Last-layer committee machines for uncertainty estimations of benthic imagery AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification

Reference 11

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This paper cites Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning.

Last-layer committee machines for uncertainty estimations of benthic imagery Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

Reference 12

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Observation 41569dde-8e5e-4447-91db-0153e43e962b · outbound

This paper cites Concrete Dropout.

Last-layer committee machines for uncertainty estimations of benthic imagery Concrete Dropout

Reference 13

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This paper cites , author Tassi, C.R.N.

Last-layer committee machines for uncertainty estimations of benthic imagery , author Tassi, C.R.N

Reference 14

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Observation 5ea783b7-97a9-4369-a0d4-1fbf5081195a · outbound

This paper cites On Calibration of Modern Neural Networks.

Last-layer committee machines for uncertainty estimations of benthic imagery On Calibration of Modern Neural Networks

Reference 15

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This paper cites Variational Bayesian Last Layers.

Last-layer committee machines for uncertainty estimations of benthic imagery Variational Bayesian Last Layers

Reference 16

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This paper cites , author Zhang, X.

Last-layer committee machines for uncertainty estimations of benthic imagery , author Zhang, X

Reference 17

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This paper cites , author Wright, D.

Last-layer committee machines for uncertainty estimations of benthic imagery , author Wright, D

Reference 18

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Last-layer committee machines for uncertainty estimations of benthic imagery Snapshot Ensembles: Train 1, get M for free

Reference 19

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Last-layer committee machines for uncertainty estimations of benthic imagery , author Brooke, B.P

Reference 20

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Last-layer committee machines for uncertainty estimations of benthic imagery , author Johansen, A.S

Reference 21

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Last-layer committee machines for uncertainty estimations of benthic imagery , author Waegeman, W

Reference 22

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Last-layer committee machines for uncertainty estimations of benthic imagery , author Althaus, F

Reference 23

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Observation 42be2e97-bb7b-44b5-bd2d-1228789ce595 · outbound

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Last-layer committee machines for uncertainty estimations of benthic imagery , author Buntine, W

Reference 24

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Last-layer committee machines for uncertainty estimations of benthic imagery , author Orenstein, E

Reference 25

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This paper cites Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles.

Last-layer committee machines for uncertainty estimations of benthic imagery Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles

Reference 26

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Last-layer committee machines for uncertainty estimations of benthic imagery Why M Heads are Better than One: Training a Diverse Ensemble of Deep Networks

Reference 27

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Last-layer committee machines for uncertainty estimations of benthic imagery , editor Wang, F

Reference 28

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Last-layer committee machines for uncertainty estimations of benthic imagery , author Wang, L

Reference 29

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Last-layer committee machines for uncertainty estimations of benthic imagery , author Kumar, M

Reference 30

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Last-layer committee machines for uncertainty estimations of benthic imagery , author Misiuk, B

Reference 31

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Last-layer committee machines for uncertainty estimations of benthic imagery , author Brown, C.J

Reference 32

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Last-layer committee machines for uncertainty estimations of benthic imagery , author Lowe, S

Reference 33

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Last-layer committee machines for uncertainty estimations of benthic imagery , year 1995

Reference 34

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This paper cites Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift.

Last-layer committee machines for uncertainty estimations of benthic imagery Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift

Reference 35

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Last-layer committee machines for uncertainty estimations of benthic imagery Uncertainty in Neural Networks: Approximately Bayesian Ensembling

Reference 36

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Last-layer committee machines for uncertainty estimations of benthic imagery title MNIST

Reference 37

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This paper cites title OneCycleLR.

Last-layer committee machines for uncertainty estimations of benthic imagery title OneCycleLR

Reference 38

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Last-layer committee machines for uncertainty estimations of benthic imagery , author Dubinsky, Z

Reference 39

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Last-layer committee machines for uncertainty estimations of benthic imagery Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 40

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Last-layer committee machines for uncertainty estimations of benthic imagery Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates

Reference 41

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Last-layer committee machines for uncertainty estimations of benthic imagery Function Space Diversity for Uncertainty Prediction via Repulsive Last-Layer Ensembles

Reference 42

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Last-layer committee machines for uncertainty estimations of benthic imagery Rethinking the Inception Architecture for Computer Vision

Reference 43

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Last-layer committee machines for uncertainty estimations of benthic imagery , year 2023

Reference 44

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Last-layer committee machines for uncertainty estimations of benthic imagery Mitigating Neural Network Overconfidence with Logit Normalization

Reference 45

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This paper cites BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning.

Last-layer committee machines for uncertainty estimations of benthic imagery BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning

Reference 46

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Last-layer committee machines for uncertainty estimations of benthic imagery , author Dai, M

Reference 47

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Last-layer committee machines for uncertainty estimations of benthic imagery , author Ma, Z

Reference 48

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Last-layer committee machines for uncertainty estimations of benthic imagery , author Misiuk, B

Reference 49

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Last-layer committee machines for uncertainty estimations of benthic imagery , author Rahman, A.U

Reference 50

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Pith citing papers

Observation 28df6a78-5c43-48df-880c-75bc96343623 · inbound

Improving Detection of Rare Nodes in Hierarchical Multi-Label Learning cites this paper.

Improving Detection of Rare Nodes in Hierarchical Multi-Label Learning Last-layer committee machines for uncertainty estimations of benthic imagery

Reference 2011

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