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Paper Citation Record · LEDGER

ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraints

As of 19 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2507.04929.

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

pith.paper-citation-record.v1
2507.04929 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:42:35.760858Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

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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

43 of 43 outbound references displayed

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External citation measurements

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Outbound references

Observation c720e496-273b-4aec-891f-7710a921dc0d · outbound

This paper cites A survey on active learning and human-in-the-loop deep learning for medical image analysis.Medical image analysis, 71:102062, 2021.

ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraints A survey on active learning and human-in-the-loop deep learning for medical image analysis.Medical image analysis, 71:102062, 2021

Reference 1

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Observation 4a6dfd13-ba88-4bc8-a806-a3a9abdd7fde · outbound

This paper cites Active learning for improved semi-supervised semantic segmentation in satellite images.

ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraints Active learning for improved semi-supervised semantic segmentation in satellite images

Reference 2

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Observation a8d74639-0d06-4b2d-8808-c202f4382d33 · outbound

This paper cites Active learning for structural reliability: Survey, general framework and benchmark.

ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraints Active learning for structural reliability: Survey, general framework and benchmark

Reference 3

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Observation a04ecfcc-1a02-4611-88f8-343100fdf18f · outbound

This paper cites Deep Bayesian active learning with image data.

ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraints Deep Bayesian active learning with image data

Reference 4

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Observation d723624f-bf87-46ee-aeb2-ae02f6c022fd · outbound

This paper cites Bayesian neural networks and density networks.

ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraints Bayesian neural networks and density networks

Reference 5

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Observation 3d5a5be6-5d2d-45cc-9b6e-8d804a8b49cb · outbound

This paper cites Bayesian learning for neural networks , volume 118.

ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraints Bayesian learning for neural networks , volume 118

Reference 6

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Observation 1d8da37d-8ca5-4cc8-92dd-6f6b51062dcc · outbound

This paper cites Deep Bayesian active learning-to-rank with relative annotation for estimation of ulcerative colitis severity.

ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraints Deep Bayesian active learning-to-rank with relative annotation for estimation of ulcerative colitis severity

Reference 7

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Observation 204ba340-c3d3-409a-9c68-5ee721528afb · outbound

This paper cites Active learning with convolutional neural networks for hyperspectral image classification using a new Bayesian approach.

ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraints Active learning with convolutional neural networks for hyperspectral image classification using a new Bayesian approach

Reference 8

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Observation f39d41b2-58ac-4189-9e09-67fae8f5113e · outbound

This paper cites Deep Bayesian Active Learning for Natural Language Processing: Results of a Large-Scale Empirical Study.

ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraints Deep Bayesian Active Learning for Natural Language Processing: Results of a Large-Scale Empirical Study

Reference 9

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Observation 21e1d8f5-77fc-4c33-9cff-fe384f13fdb5 · outbound

This paper cites Challenges in Markov chain Monte Carlo for Bayesian neural networks.

ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraints Challenges in Markov chain Monte Carlo for Bayesian neural networks

Reference 10

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Observation 8fbdee78-05b3-433f-9a1c-337c13af30f2 · outbound

This paper cites A complete recipe for stochastic gradient MCMC.

ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraints A complete recipe for stochastic gradient MCMC

Reference 11

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Observation cf36c415-6b8d-4d42-8570-17a0c3cd5c9e · outbound

This paper cites Bayesian model comparison and backprop nets.

ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraints Bayesian model comparison and backprop nets

Reference 12

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Observation 665e2081-03a8-4c6b-8292-543d28247ea8 · outbound

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

ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraints Dropout as a Bayesian approximation: Representing model uncertainty in deep learning

Reference 13

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Observation c2bce39a-15a8-41c1-90bd-692228e9dd87 · outbound

This paper cites Deep Bayesian Active Learning, A Brief Survey on Recent Advances.

ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraints Deep Bayesian Active Learning, A Brief Survey on Recent Advances

Reference 14

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Observation e2859f89-e0a0-4b15-af1f-2c709352a3f2 · outbound

This paper cites A survey on Bayesian deep learning.

ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraints A survey on Bayesian deep learning

Reference 15

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This paper cites A review of uncertainty quantification in deep learning: Techniques, applications and challenges.

ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraints A review of uncertainty quantification in deep learning: Techniques, applications and challenges

Reference 16

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ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraints Uncertainty in deep learning

Reference 17

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ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraints A mathematical theory of communication

Reference 18

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ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraints Elementary applied statistics

Reference 19

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This paper cites Batchbald: Efficient and diverse batch acquisition for deep Bayesian active learning.

ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraints Batchbald: Efficient and diverse batch acquisition for deep Bayesian active learning

Reference 20

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ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraints Speeding Up BatchBALD: A k-BALD Family of Approximations for Active Learning

Reference 21

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ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraints Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds

Reference 22

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ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraints Bayesian batch active learning as sparse subset approximation

Reference 23

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Observation 73dee689-0639-401e-8c71-78fb735b7a98 · outbound

This paper cites Stochastic Batch Acquisition: A Simple Baseline for Deep Active Learning.

ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraints Stochastic Batch Acquisition: A Simple Baseline for Deep Active Learning

Reference 24

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ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraints Active learning for cost-sensitive classification

Reference 25

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ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraints Learning cost-sensitive active classifiers

Reference 26

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ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraints Optimised probabilistic active learning (OPAL) for fast, non-myopic, cost-sensitive active classification

Reference 27

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ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraints Batch multi-fidelity active learning with budget constraints

Reference 28

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ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraints Aid: A benchmark data set for performance evaluation of aerial scene classification

Reference 29

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ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraints xView: Objects in Context in Overhead Imagery

Reference 30

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ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraints Dota: A large-scale dataset for object detection in aerial images

Reference 31

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Observation 50b01f1b-6882-4246-af19-d52a66305836 · outbound

This paper cites SPAGRI-AI: Smart precision agriculture dataset of aerial images at different heights for crop and weed detection using super-resolution.

ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraints SPAGRI-AI: Smart precision agriculture dataset of aerial images at different heights for crop and weed detection using super-resolution

Reference 32

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ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraints Estimating building energy efficiency from street view imagery, aerial imagery, and land surface temperature data

Reference 33

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Observation d47c070f-0765-4df2-84b5-1811068975a3 · outbound

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ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraints Review of 50 years of EU energy efficiency policies for buildings

Reference 34

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Observation d5965152-752c-4b20-b71c-8b9387d7a2a7 · outbound

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ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraints The relationship be- tween operational energy demand and embodied energy in Dutch residential buildings

Reference 35

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation c40ad079-53db-4eba-b1c7-d4fedf6a557c · outbound

This paper cites PDOK - Publieke Dienstverlening Op de Kaart.

ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraints PDOK - Publieke Dienstverlening Op de Kaart

Reference 36

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verified fuzzy
raw_fallback, observed 2026-08-06T19:42:35.974412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation f9722cf5-f9de-4ece-a3f4-216b1efd7210 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraints DINOv2: Learning Robust Visual Features without Supervision

Reference 37

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unresolved
no resolver link, observed 2026-08-06T19:42:35.737636Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:42:35.737636Z digest=sha256:bff64b9782b63bb99c8515d80d84376c0d815ac29243ade1417b66c88d32f35e

Observation bb41497e-843c-4268-a1f7-e54071d31cec · outbound

This paper cites Understanding architecture age and style through deep learning.

ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraints Understanding architecture age and style through deep learning

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:42:35.960101Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:42:35.741679Z digest=sha256:fde2ac87c5a1c4fa9ea8c784c91bcc7f2460fb819f157ba0d0c230294b773718

Observation 107d69e5-1175-4dd0-a344-a080ca7c8eb0 · outbound

This paper cites Bayesian learning via stochastic gradient Langevin dynamics.

ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraints Bayesian learning via stochastic gradient Langevin dynamics

Reference 39

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verified fuzzy
raw_fallback, observed 2026-08-06T19:42:35.947437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:42:35.745407Z digest=sha256:f07471f2d646e9c67c807ea31144ddc12063a07386f48e5557d016504e6ff3f1

Observation 0e6cb7c3-628f-4894-8022-ae5b88d1f592 · outbound

This paper cites Stochastic gradient hamiltonian monte carlo.

ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraints Stochastic gradient hamiltonian monte carlo

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:42:35.934877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:42:35.749133Z digest=sha256:7f081335055cf5b993cae9c59af82936e1f7eac3ca7cd4e26a6908fa705d012a

Observation 67f285c1-9996-4a57-be40-54aa225831cb · outbound

This paper cites Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning.

ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraints Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning

Reference 41

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unresolved
no resolver link, observed 2026-08-06T19:42:35.752988Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:42:35.752988Z digest=sha256:95bccfe31568d693ac5201c408f5ecd3ffd9618fe9bc3ffafcb07489752e3751

Observation dc7fc13f-e8da-40a6-b0ff-1cff097fef8c · outbound

This paper cites 3D BAG viewer.

ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraints 3D BAG viewer

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:42:35.921804Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:42:35.757125Z digest=sha256:f00c16912f3e6c1ecaa063e77e98f9506465c5e5129e3e7d834ea37a04c39815

Observation dc5c49a8-8756-4554-9b5a-4a35e210b890 · outbound

This paper cites R VO - Rijksdienst voor Ondernemend Nederland.

ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraints R VO - Rijksdienst voor Ondernemend Nederland

Reference 43

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verified fuzzy
raw_fallback, observed 2026-08-06T19:42:35.909015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:42:35.760858Z digest=sha256:ad706dc2e9a38e341f3769adf43351ab3dbafb2f6804afa567a2d0a75a8db3e1

Pith citing papers

No inbound Pith citation observations are available.