Pith. sign in

Paper Citation Record · LEDGER

A Survey of Deep Active Learning

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2009.00236.

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

pith.paper-citation-record.v1
2009.00236 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:57:58.296769Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

39
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation a369790b-103a-41ef-a243-aae1bcd63d17 · inbound

RLS3: RL-Based Synthetic Sample Selection to Enhance Spatial Reasoning in Vision-Language Models for Indoor Autonomous Perception cites this paper.

RLS3: RL-Based Synthetic Sample Selection to Enhance Spatial Reasoning in Vision-Language Models for Indoor Autonomous Perception A Survey of Deep Active Learning

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-09T22:09:40.668196Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T22:09:40.668196Z digest=sha256:c2ae92093a63901dc01e1ec94ead120d6adac7834d12f5c9ba33d74244d1e409

Observation a7344540-51d3-4a68-83cc-1e2314fb5f77 · inbound

JailbreaksOverTime: Detecting Jailbreak Attacks Under Distribution Shift cites this paper.

JailbreaksOverTime: Detecting Jailbreak Attacks Under Distribution Shift A Survey of Deep Active Learning

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-16T05:57:58.296769Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T05:57:58.296769Z digest=sha256:8ab2dee1f76226fd7d7fbcbe814291ec94f422778b47cc6dc70362cebeb0f085

Observation ff43585f-c64b-4cea-9a51-fa38211608b4 · inbound

Combining Bayesian Inference and Reinforcement Learning for Agent Decision Making: A Review cites this paper.

Combining Bayesian Inference and Reinforcement Learning for Agent Decision Making: A Review A Survey of Deep Active Learning

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-15T22:17:38.915977Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T22:17:38.915977Z digest=sha256:3d9bd781b45a76e79375be5e26e4e4a5d3db92c24d1ce95f6d7629a8737d5c5f

Observation ee088fca-4351-4caf-9fb9-8a37d3a29226 · inbound

A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules cites this paper.

A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules A Survey of Deep Active Learning

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-07T15:08:18.607970Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:08:18.607970Z digest=sha256:d4d03785e0453aa786147ab6a80827a17725f345f1d7a395add76a991eaa2ccd

Observation 4d470663-ae97-4c76-b8d2-f283eb0073bc · inbound

X-Factor: Quality Is a Dataset-Intrinsic Property cites this paper.

X-Factor: Quality Is a Dataset-Intrinsic Property A Survey of Deep Active Learning

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-07T13:06:55.440582Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:06:55.440582Z digest=sha256:7e1e3b0a9adf2a4d775a054fdfb029f0187add31d25592597708eed571e4f247

Observation ab0d8052-b473-4e96-bcee-68b82a26a6bc · inbound

Exploring Spatial Diversity for Region-based Active Learning cites this paper.

Exploring Spatial Diversity for Region-based Active Learning A Survey of Deep Active Learning

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-06T14:56:38.477578Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T14:56:38.477578Z digest=sha256:3935faebb32f3685579e1a2cd9522c6841165821af05ba65a24489b4a35e2f85

Observation 0063ea1d-4024-4266-b73d-0d76d27fffa6 · inbound

Deep Active Learning for Lung Disease Severity Classification from Chest X-rays: Learning with Less Data in the Presence of Class Imbalance cites this paper.

Deep Active Learning for Lung Disease Severity Classification from Chest X-rays: Learning with Less Data in the Presence of Class Imbalance A Survey of Deep Active Learning

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-05T14:30:57.291301Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:30:57.291301Z digest=sha256:5c4304444227a6074ddfd63a140f69344d0d9e7e92876d80b5b0af241c360d5b

Observation 0ff3b5d9-3ec3-4599-8db4-4b629047983a · inbound

Revisiting Active Sequential Prediction-Powered Mean Estimation cites this paper.

Revisiting Active Sequential Prediction-Powered Mean Estimation A Survey of Deep Active Learning

Reference 17

Resolution
metadata mismatch
arxiv_id, observed 2026-05-10T03:29:21.615672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-10T03:28:16.667259Z digest=sha256:4bb841a2e219e145957dfbd49674ef05c62281b352bb3710f2e51ae169bc4130

Observation d4ae0856-32b0-44a2-8d82-267d9142985b · inbound

Data-Efficient Neural Operator Training via Physics-Based Active Learning cites this paper.

Data-Efficient Neural Operator Training via Physics-Based Active Learning A Survey of Deep Active Learning

Reference 12

Resolution
verified exact
arxiv_id, observed 2026-05-21T05:33:58.517936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-21T05:33:00.817862Z digest=sha256:f2edb1f29a8627656cebc13ab3ea44ca5d3161ee835dee8be8c43775e5d4fb19

Observation 3897c957-6a8b-4768-b885-a0a3b0c84bea · inbound

Learning Where to Simulate: Generative Active Sampling for Online PDE Surrogate Training cites this paper.

Learning Where to Simulate: Generative Active Sampling for Online PDE Surrogate Training A Survey of Deep Active Learning

Reference 39

Resolution
verified exact
arxiv_id, observed 2026-06-27T17:21:06.426626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-06-27T17:20:06.255050Z digest=sha256:ee306791352642ff5c4accf3330eb10f0fb1f433a752a1d396a1f1244aead528