Paper Citation Record · LEDGER

Uncertainty Herding: One Active Learning Method for All Label Budgets

As of 15 August 2026, this Paper Citation Record lists 61 of 61 outbound references and 1 inbound Pith citation observation for arXiv:2412.20644.

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

pith.paper-citation-record.v1
2412.20644 v2

Coverage vector

measured 61 of 61 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T23:21:16.912001Z

measured 62 of 62 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T14:42:43.176932Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

61 of 61 outbound references displayed

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verified fuzzy43
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External citation measurements

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Outbound references

Observation 7d050665-9d97-4fd8-a6a7-61db7b1eaab7 · outbound

This paper cites Active distance-based clustering using k-medoids.

Uncertainty Herding: One Active Learning Method for All Label Budgets Active distance-based clustering using k-medoids

Reference 1

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Observation 13a44511-d4f1-4577-936b-2fef89684d8f · outbound

This paper cites On warm-starting neural network training.

Uncertainty Herding: One Active Learning Method for All Label Budgets On warm-starting neural network training

Reference 2

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Observation de59d67d-efad-43b7-8598-17ffe72e468d · outbound

This paper cites Gone fishing: Neural active learning with fisher embeddings.

Uncertainty Herding: One Active Learning Method for All Label Budgets Gone fishing: Neural active learning with fisher embeddings

Reference 3

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Observation 12390363-223c-435c-a847-68983b543385 · outbound

This paper cites Deep batch active learning by diverse, uncertain gradient lower bounds.

Uncertainty Herding: One Active Learning Method for All Label Budgets Deep batch active learning by diverse, uncertain gradient lower bounds

Reference 4

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Observation 77560e95-3062-4202-877d-11ba81527a22 · outbound

This paper cites Generalized coverage for more robust low-budget active learning.

Uncertainty Herding: One Active Learning Method for All Label Budgets Generalized coverage for more robust low-budget active learning

Reference 5

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Observation a3ea6b28-7c0a-4c13-85bb-89b88b1d44d9 · outbound

This paper cites Batch active learning using determinantal point processes.

Uncertainty Herding: One Active Learning Method for All Label Budgets Batch active learning using determinantal point processes

Reference 6

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Observation 23ec4721-bf77-4f75-b073-6b25f77b44fa · outbound

This paper cites Emerging properties in self-supervised vision transformers.

Uncertainty Herding: One Active Learning Method for All Label Budgets Emerging properties in self-supervised vision transformers

Reference 7

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Observation 84ffd254-6c76-470a-ab92-95b8bc4e55cd · outbound

This paper cites A simple framework for contrastive learning of visual representations.

Uncertainty Herding: One Active Learning Method for All Label Budgets A simple framework for contrastive learning of visual representations

Reference 8

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Observation 3b7090d2-eb97-4bee-9ef4-3f28f1a04716 · outbound

This paper cites A closer look at few-shot classification.

Uncertainty Herding: One Active Learning Method for All Label Budgets A closer look at few-shot classification

Reference 9

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Observation c700458b-7b24-4eb5-837b-1b6501b95dc2 · outbound

This paper cites Super-samples from kernel herding.

Uncertainty Herding: One Active Learning Method for All Label Budgets Super-samples from kernel herding

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:21:17.582771Z

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Observation 068a1c07-cc29-4c95-b5fd-a771c92c3997 · outbound

This paper cites On the mathematical foundations of learning.

Uncertainty Herding: One Active Learning Method for All Label Budgets On the mathematical foundations of learning

Reference 11

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Observation d68214c4-afa4-4474-b496-511eb2afe199 · outbound

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

Uncertainty Herding: One Active Learning Method for All Label Budgets ImageNet : A large-scale hierarchical image database

Reference 12

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Observation 655837aa-fc67-4898-81e2-3f17be991d47 · outbound

This paper cites Dual strategy active learning.

Uncertainty Herding: One Active Learning Method for All Label Budgets Dual strategy active learning

Reference 13

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Observation c8979c04-bf49-4523-af46-2ad92bbffad8 · outbound

This paper cites Bridging diversity and uncertainty in active learning with self-supervised pre-training.

Uncertainty Herding: One Active Learning Method for All Label Budgets Bridging diversity and uncertainty in active learning with self-supervised pre-training

Reference 14

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Observation f743f2a4-6912-4e7c-a1a5-2839000f44d1 · outbound

This paper cites Selecting influential examples: Active learning with expected model output changes.

Uncertainty Herding: One Active Learning Method for All Label Budgets Selecting influential examples: Active learning with expected model output changes

Reference 15

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Observation 2e95d956-34f6-4538-98f6-04fa3ba9af73 · outbound

This paper cites Deep bayesian active learning with image data.

Uncertainty Herding: One Active Learning Method for All Label Budgets Deep bayesian active learning with image data

Reference 16

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Observation 9a10f11d-82a3-47e0-9816-81adbbde6e47 · outbound

This paper cites Unraveling meta-learning: Understanding feature representations for few-shot tasks.

Uncertainty Herding: One Active Learning Method for All Label Budgets Unraveling meta-learning: Understanding feature representations for few-shot tasks

Reference 17

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Observation b7aa4aef-9414-4f38-a09c-d29351f3adfa · outbound

This paper cites On calibration of modern neural networks.

Uncertainty Herding: One Active Learning Method for All Label Budgets On calibration of modern neural networks

Reference 18

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Observation b079f236-66a6-41b1-bf25-7f0236d6c7fc · outbound

This paper cites Optimistic active-learning using mutual information.

Uncertainty Herding: One Active Learning Method for All Label Budgets Optimistic active-learning using mutual information

Reference 19

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Observation c8cd9555-0a5d-4f35-862b-11cd7ab9071c · outbound

This paper cites How to select which active learning strategy is best suited for your specific problem and budget.

Uncertainty Herding: One Active Learning Method for All Label Budgets How to select which active learning strategy is best suited for your specific problem and budget

Reference 20

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Observation 0d32646a-2d19-4963-a131-881483e15f21 · outbound

This paper cites Active learning on a budget: Opposite strategies suit high and low budgets.

Uncertainty Herding: One Active Learning Method for All Label Budgets Active learning on a budget: Opposite strategies suit high and low budgets

Reference 21

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Observation a1c411e7-5c33-40cb-8132-044dbfe9d431 · outbound

This paper cites Deep residual learning for image recognition.

Uncertainty Herding: One Active Learning Method for All Label Budgets Deep residual learning for image recognition

Reference 22

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Observation a69de62c-160f-478c-8b70-a9667adee754 · outbound

This paper cites Active and continuous exploration with deep neural networks and expected model output changes.

Uncertainty Herding: One Active Learning Method for All Label Budgets Active and continuous exploration with deep neural networks and expected model output changes

Reference 23

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This paper cites a ding, Erik Rodner, Alexander Freytag, Oliver Mothes, Bj \.

Uncertainty Herding: One Active Learning Method for All Label Budgets a ding, Erik Rodner, Alexander Freytag, Oliver Mothes, Bj \

Reference 24

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This paper cites Finding groups in data: an introduction to cluster analysis.

Uncertainty Herding: One Active Learning Method for All Label Budgets Finding groups in data: an introduction to cluster analysis

Reference 25

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Observation 674b360c-c89f-43e4-9a81-18eb3ea1d90d · outbound

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

Uncertainty Herding: One Active Learning Method for All Label Budgets Batchbald: Efficient and diverse batch acquisition for deep bayesian active learning

Reference 26

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Observation 971bc1f1-a9d9-4322-b1d6-d177a934829d · outbound

This paper cites Learning multiple layers of features from tiny images, 2009.

Uncertainty Herding: One Active Learning Method for All Label Budgets Learning multiple layers of features from tiny images, 2009

Reference 27

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This paper cites Cifar-100 (canadian institute for advanced research).

Uncertainty Herding: One Active Learning Method for All Label Budgets Cifar-100 (canadian institute for advanced research)

Reference 28

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Observation 75426e4b-4c76-4e1d-98d9-e93382e9ca97 · outbound

This paper cites Tidal: Learning training dynamics for active learning.

Uncertainty Herding: One Active Learning Method for All Label Budgets Tidal: Learning training dynamics for active learning

Reference 29

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Observation 243a7d48-2e77-4f63-821a-9195b58f7f61 · outbound

This paper cites Heterogeneous uncertainty sampling for supervised learning.

Uncertainty Herding: One Active Learning Method for All Label Budgets Heterogeneous uncertainty sampling for supervised learning

Reference 30

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Observation b46282ae-ea6e-45f7-aef2-018680d2327e · outbound

This paper cites A sequential algorithm for training text classifiers.

Uncertainty Herding: One Active Learning Method for All Label Budgets A sequential algorithm for training text classifiers

Reference 31

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This paper cites Low budget active learning via wasserstein distance: An integer programming approach.

Uncertainty Herding: One Active Learning Method for All Label Budgets Low budget active learning via wasserstein distance: An integer programming approach

Reference 32

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This paper cites Tiny imagenet, 2017.

Uncertainty Herding: One Active Learning Method for All Label Budgets Tiny imagenet, 2017

Reference 33

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This paper cites Making look-ahead active learning strategies feasible with neural tangent kernels.

Uncertainty Herding: One Active Learning Method for All Label Budgets Making look-ahead active learning strategies feasible with neural tangent kernels

Reference 34

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Uncertainty Herding: One Active Learning Method for All Label Budgets Obtaining well calibrated probabilities using bayesian binning

Reference 35

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Uncertainty Herding: One Active Learning Method for All Label Budgets Unresolved cited work

Reference 36

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Uncertainty Herding: One Active Learning Method for All Label Budgets Active learning using pre-clustering

Reference 37

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Uncertainty Herding: One Active Learning Method for All Label Budgets Active learning by feature mixing

Reference 38

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Observation 69b14f2c-0ff7-44e4-92bf-faae3885ef09 · outbound

This paper cites Moment matching for multi-source domain adaptation.

Uncertainty Herding: One Active Learning Method for All Label Budgets Moment matching for multi-source domain adaptation

Reference 39

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Uncertainty Herding: One Active Learning Method for All Label Budgets Toward optimal active learning through monte carlo estimation of error reduction

Reference 40

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Uncertainty Herding: One Active Learning Method for All Label Budgets Active hidden M arkov models for information extraction

Reference 41

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This paper cites Fast k-medoids clustering in R ust and P ython.

Uncertainty Herding: One Active Learning Method for All Label Budgets Fast k-medoids clustering in R ust and P ython

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:21:17.177243Z

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Uncertainty Herding: One Active Learning Method for All Label Budgets Faster k-medoids clustering: improving the PAM , CLARA , and CLARANS algorithms

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:21:17.164123Z

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Uncertainty Herding: One Active Learning Method for All Label Budgets Fast and eager k-medoids clustering: O(k) runtime improvement of the PAM , CLARA , and CLARANS algorithms

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:21:17.151650Z

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Uncertainty Herding: One Active Learning Method for All Label Budgets Active learning for convolutional neural networks: A core-set approach

Reference 45

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Uncertainty Herding: One Active Learning Method for All Label Budgets Active learning literature survey

Reference 46

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This paper cites An analysis of active learning strategies for sequence labeling tasks.

Uncertainty Herding: One Active Learning Method for All Label Budgets An analysis of active learning strategies for sequence labeling tasks

Reference 47

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This paper cites Multiple-instance active learning.

Uncertainty Herding: One Active Learning Method for All Label Budgets Multiple-instance active learning

Reference 48

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Observation cae86cb2-2ae9-4d9e-89ce-e70843f822e8 · outbound

This paper cites Training data-efficient image transformers & distillation through attention.

Uncertainty Herding: One Active Learning Method for All Label Budgets Training data-efficient image transformers & distillation through attention

Reference 49

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Uncertainty Herding: One Active Learning Method for All Label Budgets Active clustering of biological sequences

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:21:17.086222Z

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Observation bf70c465-987c-40f4-9f60-7e52b474d846 · outbound

This paper cites A new active labeling method for deep learning.

Uncertainty Herding: One Active Learning Method for All Label Budgets A new active labeling method for deep learning

Reference 51

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Observation 94923c48-3f84-4f4b-8bf5-93af0b744d89 · outbound

This paper cites SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning.

Uncertainty Herding: One Active Learning Method for All Label Budgets SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning

Reference 52

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This paper cites Active finetuning: Exploiting annotation budget in the pretraining-finetuning paradigm.

Uncertainty Herding: One Active Learning Method for All Label Budgets Active finetuning: Exploiting annotation budget in the pretraining-finetuning paradigm

Reference 53

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Observation a55c9cf7-3f68-4d0c-b70b-ad76eaa13064 · outbound

This paper cites Representative sampling for text classification using support vector machines.

Uncertainty Herding: One Active Learning Method for All Label Budgets Representative sampling for text classification using support vector machines

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:21:17.048539Z

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Observation 5429a32d-0264-4339-82bc-ae4f93509e69 · outbound

This paper cites Active learning through a covering lens.

Uncertainty Herding: One Active Learning Method for All Label Budgets Active learning through a covering lens

Reference 55

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Observation d6472a4a-3f92-4587-85ed-508c8b545b28 · outbound

This paper cites Diverse mini-batch Active Learning.

Uncertainty Herding: One Active Learning Method for All Label Budgets Diverse mini-batch Active Learning

Reference 56

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Observation 48e296e6-e9b0-44c5-a7d1-059beadc3694 · outbound

This paper cites Combining active learning and semi-supervised learning using gaussian fields and harmonic functions.

Uncertainty Herding: One Active Learning Method for All Label Budgets Combining active learning and semi-supervised learning using gaussian fields and harmonic functions

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:21:17.024802Z

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Observation f01c8b46-f3ca-4c6b-8357-4f9da931764e · outbound

This paper cites write newline.

Uncertainty Herding: One Active Learning Method for All Label Budgets write newline

Reference 58

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Observation 7b725627-d515-4386-840f-b57a646780b6 · outbound

This paper cites @esa (Ref.

Uncertainty Herding: One Active Learning Method for All Label Budgets @esa (Ref

Reference 59

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Observation 8584b0fc-4a04-4591-95c1-eddbaa689e7e · outbound

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Uncertainty Herding: One Active Learning Method for All Label Budgets Unresolved cited work

Reference 60

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Observation 1c13b8dd-c5ff-4f99-9fea-2158c5c107ce · outbound

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Uncertainty Herding: One Active Learning Method for All Label Budgets Unresolved cited work

Reference 61

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Pith citing papers

Observation 20c908a5-9942-4ef8-9004-1e944a2e5348 · inbound

Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification cites this paper.

Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification Uncertainty Herding: One Active Learning Method for All Label Budgets

Reference 6

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