Paper Citation Record · LEDGER

Greedy Volume Maximization of Gradient Embeddings for Long-Tailed Frame-Level Bioacoustic Active Learning

As of 10 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2607.13555.

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

pith.paper-citation-record.v1
2607.13555 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T04:53:33.069100Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

39 of 39 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2f52c596-2c51-4517-8101-307e96dbd2de · outbound

This paper cites Computational bioacoustics with deep learning: a review and roadmap,.

Greedy Volume Maximization of Gradient Embeddings for Long-Tailed Frame-Level Bioacoustic Active Learning Computational bioacoustics with deep learning: a review and roadmap,

Reference 1

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Observation 4bd0a9ec-9b2c-4d19-9a45-a6af392ed6ec · outbound

This paper cites Active learning for sound event detection,.

Greedy Volume Maximization of Gradient Embeddings for Long-Tailed Frame-Level Bioacoustic Active Learning Active learning for sound event detection,

Reference 2

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Observation 2b0b2534-65e9-4203-a679-6a93d4f56d42 · outbound

This paper cites From weak to strong sound event labels using adaptive change-point detection and active learning,.

Greedy Volume Maximization of Gradient Embeddings for Long-Tailed Frame-Level Bioacoustic Active Learning From weak to strong sound event labels using adaptive change-point detection and active learning,

Reference 3

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Observation 0e746d90-da6d-474d-904b-ba37a83f6e55 · outbound

This paper cites Active few-shot learning for sound event detection,.

Greedy Volume Maximization of Gradient Embeddings for Long-Tailed Frame-Level Bioacoustic Active Learning Active few-shot learning for sound event detection,

Reference 4

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Observation ac7e1327-e5fd-4390-a09f-192ec997f609 · outbound

This paper cites Active learning for sound event classification using monte-carlo dropout and PANN embeddings,.

Greedy Volume Maximization of Gradient Embeddings for Long-Tailed Frame-Level Bioacoustic Active Learning Active learning for sound event classification using monte-carlo dropout and PANN embeddings,

Reference 5

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Observation d6c84180-90f7-4a84-8e75-684b7dcb00a5 · outbound

This paper cites Active learning for sound event classification using bayesian neural networks with gaussian variational posterior,.

Greedy Volume Maximization of Gradient Embeddings for Long-Tailed Frame-Level Bioacoustic Active Learning Active learning for sound event classification using bayesian neural networks with gaussian variational posterior,

Reference 6

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Observation 93417bf9-6930-42ef-ab2f-e519c29e844d · outbound

This paper cites Online Active Learning For Sound Event Detection.

Greedy Volume Maximization of Gradient Embeddings for Long-Tailed Frame-Level Bioacoustic Active Learning Online Active Learning For Sound Event Detection

Reference 7

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Observation 23a23189-6d4c-4a21-8386-27b8efee0d56 · outbound

This paper cites Active few-shot learning for rare bioacoustic feature annotation,.

Greedy Volume Maximization of Gradient Embeddings for Long-Tailed Frame-Level Bioacoustic Active Learning Active few-shot learning for rare bioacoustic feature annotation,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-02T04:53:29.649796Z

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Observation 14e93aa3-db43-4044-b89e-fd24b264f1c0 · outbound

This paper cites Aggregation strategies for efficient annotation of bioacoustic sound events using active learning,.

Greedy Volume Maximization of Gradient Embeddings for Long-Tailed Frame-Level Bioacoustic Active Learning Aggregation strategies for efficient annotation of bioacoustic sound events using active learning,

Reference 9

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Observation 62e3d542-aaf6-4477-b02a-0575ffcfadd7 · outbound

This paper cites Reducing class imbalance during active learning for named entity annotation,.

Greedy Volume Maximization of Gradient Embeddings for Long-Tailed Frame-Level Bioacoustic Active Learning Reducing class imbalance during active learning for named entity annotation,

Reference 10

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Observation ed78a4aa-f268-4899-a361-387cb7d981f5 · outbound

This paper cites VaB-AL: Incorporating class imbalance and difficulty with variational Bayes for active learning,.

Greedy Volume Maximization of Gradient Embeddings for Long-Tailed Frame-Level Bioacoustic Active Learning VaB-AL: Incorporating class imbalance and difficulty with variational Bayes for active learning,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-02T04:53:30.171135Z

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Observation 42c81b61-c9b9-4509-bd6b-812b800ca84d · outbound

This paper cites Class-balanced loss based on effective number of samples,.

Greedy Volume Maximization of Gradient Embeddings for Long-Tailed Frame-Level Bioacoustic Active Learning Class-balanced loss based on effective number of samples,

Reference 12

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Observation 5060f618-af35-47ee-a5b1-91902b527435 · outbound

This paper cites Class- balanced active learning for image classification,.

Greedy Volume Maximization of Gradient Embeddings for Long-Tailed Frame-Level Bioacoustic Active Learning Class- balanced active learning for image classification,

Reference 13

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Observation 76d3ef91-97a6-4554-bcf7-709c7772c84e · outbound

This paper cites Online adaptive asymmetric active learning for budgeted imbalanced data,.

Greedy Volume Maximization of Gradient Embeddings for Long-Tailed Frame-Level Bioacoustic Active Learning Online adaptive asymmetric active learning for budgeted imbalanced data,

Reference 14

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Observation ce5e3176-db62-445d-b13d-362b73baae33 · outbound

This paper cites Generative active learning for long-tailed instance segmentation,.

Greedy Volume Maximization of Gradient Embeddings for Long-Tailed Frame-Level Bioacoustic Active Learning Generative active learning for long-tailed instance segmentation,

Reference 15

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Observation 615d997f-07d2-4608-b68d-7c0f25a61ada · outbound

This paper cites Deep long-tailed learning: A survey,.

Greedy Volume Maximization of Gradient Embeddings for Long-Tailed Frame-Level Bioacoustic Active Learning Deep long-tailed learning: A survey,

Reference 16

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Observation cfcafaf0-5b71-4be8-8206-5e89c013ce6c · outbound

This paper cites A sequential algorithm for training text classifiers,.

Greedy Volume Maximization of Gradient Embeddings for Long-Tailed Frame-Level Bioacoustic Active Learning A sequential algorithm for training text classifiers,

Reference 17

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Observation 37973187-4cb3-4bc3-b6ab-ea0b455743b5 · outbound

This paper cites Settles,Active Learning, ser.

Greedy Volume Maximization of Gradient Embeddings for Long-Tailed Frame-Level Bioacoustic Active Learning Settles,Active Learning, ser

Reference 18

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Observation 837471f4-4cbc-494e-8795-43b2020a10f1 · outbound

This paper cites Query by committee,.

Greedy Volume Maximization of Gradient Embeddings for Long-Tailed Frame-Level Bioacoustic Active Learning Query by committee,

Reference 19

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Observation 48c4db71-903e-4e2a-b095-a291234e9f0e · outbound

This paper cites Selective sampling using the query by committee algorithm,.

Greedy Volume Maximization of Gradient Embeddings for Long-Tailed Frame-Level Bioacoustic Active Learning Selective sampling using the query by committee algorithm,

Reference 20

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Observation 26d77c8d-0de6-4854-b16d-bf9e746cad30 · outbound

This paper cites Clustering to minimize the maximum intercluster distance,.

Greedy Volume Maximization of Gradient Embeddings for Long-Tailed Frame-Level Bioacoustic Active Learning Clustering to minimize the maximum intercluster distance,

Reference 21

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Observation a9bb6de1-a297-46ed-a70c-c592a1ee1e74 · outbound

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Greedy Volume Maximization of Gradient Embeddings for Long-Tailed Frame-Level Bioacoustic Active Learning Active learning for convolutional neural networks: A core-set approach,

Reference 22

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Observation 3ff3cf2c-a338-411c-a68b-3a49e39c7255 · outbound

This paper cites Deep Bayesian active learning with image data,.

Greedy Volume Maximization of Gradient Embeddings for Long-Tailed Frame-Level Bioacoustic Active Learning Deep Bayesian active learning with image data,

Reference 23

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Observation e3b36a90-7017-4bf6-93db-60b049f24c12 · outbound

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Greedy Volume Maximization of Gradient Embeddings for Long-Tailed Frame-Level Bioacoustic Active Learning The power of ensembles for active learning in image classification,

Reference 24

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Observation c1f2850d-b832-4d50-8552-eed3536fb0e0 · outbound

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Greedy Volume Maximization of Gradient Embeddings for Long-Tailed Frame-Level Bioacoustic Active Learning BatchBALD: Efficient and diverse batch acquisition for deep Bayesian active learning,

Reference 25

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Observation ad33a35b-583f-487e-9383-ea3ce2270def · outbound

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Greedy Volume Maximization of Gradient Embeddings for Long-Tailed Frame-Level Bioacoustic Active Learning Batch active learning at scale,

Reference 26

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Observation 34391ed9-5bfe-447e-8f37-918923297ebf · outbound

This paper cites Batch Active Learning Using Determinantal Point Processes.

Greedy Volume Maximization of Gradient Embeddings for Long-Tailed Frame-Level Bioacoustic Active Learning Batch Active Learning Using Determinantal Point Processes

Reference 27

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Observation 0bb78b73-615b-4e9f-b0be-c54b005af577 · outbound

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Greedy Volume Maximization of Gradient Embeddings for Long-Tailed Frame-Level Bioacoustic Active Learning Deep batch active learning by diverse, uncertain gradient lower bounds,

Reference 28

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Greedy Volume Maximization of Gradient Embeddings for Long-Tailed Frame-Level Bioacoustic Active Learning k-means++: the advantages of careful seeding,

Reference 29

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Observation e6590db0-6399-4040-9741-81b9ab52663c · outbound

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Greedy Volume Maximization of Gradient Embeddings for Long-Tailed Frame-Level Bioacoustic Active Learning Monte Carlo Markov chain algorithms for sampling strongly Rayleigh distributions and determinantal point processes,

Reference 30

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Observation 14eafaf1-5112-4048-a0e4-05d7d36e8d38 · outbound

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Greedy Volume Maximization of Gradient Embeddings for Long-Tailed Frame-Level Bioacoustic Active Learning Determinantal point processes for machine learning,

Reference 31

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Observation 5e274adb-1fda-4130-8433-14bd3568851c · outbound

This paper cites Near-optimal sensor placements in Gaussian processes: Theory, efficient algorithms and empirical studies,.

Greedy Volume Maximization of Gradient Embeddings for Long-Tailed Frame-Level Bioacoustic Active Learning Near-optimal sensor placements in Gaussian processes: Theory, efficient algorithms and empirical studies,

Reference 32

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This paper cites An analysis of approximations for maximizing submodular set functions - I,.

Greedy Volume Maximization of Gradient Embeddings for Long-Tailed Frame-Level Bioacoustic Active Learning An analysis of approximations for maximizing submodular set functions - I,

Reference 33

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Observation 9f8bd02d-910f-4fcc-a2b3-ba0d17394f68 · outbound

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Greedy Volume Maximization of Gradient Embeddings for Long-Tailed Frame-Level Bioacoustic Active Learning HyenaSET: Hyena sound event transcripts and benchmark animal2vec performance for parsing animal communication,

Reference 34

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Greedy Volume Maximization of Gradient Embeddings for Long-Tailed Frame-Level Bioacoustic Active Learning animal2vec and meerkat: A self-supervised transformer for rare-event raw audio input and a large-scale reference dataset for bioacoustics,

Reference 35

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Observation 1f69b8e0-26fc-45df-bc99-465079cdbd47 · outbound

This paper cites An active learning method using clustering and committee-based sample selection for sound event classification,.

Greedy Volume Maximization of Gradient Embeddings for Long-Tailed Frame-Level Bioacoustic Active Learning An active learning method using clustering and committee-based sample selection for sound event classification,

Reference 36

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Observation 21d7753a-35cd-43ce-a753-549c24217496 · outbound

This paper cites Hybrid disagreement-diversity active learning for bioacoustic sound event detection,.

Greedy Volume Maximization of Gradient Embeddings for Long-Tailed Frame-Level Bioacoustic Active Learning Hybrid disagreement-diversity active learning for bioacoustic sound event detection,

Reference 37

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Observation b920f332-6e60-4477-a062-5fb3e57105e8 · outbound

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Greedy Volume Maximization of Gradient Embeddings for Long-Tailed Frame-Level Bioacoustic Active Learning Submodularity in data subset selection and active learning,

Reference 38

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Observation 8ddf6ecb-3a3d-4ad8-b5a1-e7c0aba75a5b · outbound

This paper cites A simple sequentially rejective multiple test procedure,.

Greedy Volume Maximization of Gradient Embeddings for Long-Tailed Frame-Level Bioacoustic Active Learning A simple sequentially rejective multiple test procedure,

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-02T04:53:33.069100Z

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Pith citing papers

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