Paper Citation Record · LEDGER

LPLgrad: Optimizing Active Learning Through Gradient Norm Sample Selection and Auxiliary Model Training

As of 13 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2411.15217.

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

pith.paper-citation-record.v1
2411.15217 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T16:22:47.921929Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+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

25 of 25 outbound references displayed

verified exact1
verified fuzzy12
unresolved12
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ee1499d3-1e88-4390-afe7-46c0e8ad2e47 · outbound

This paper cites A Comparative Survey of Deep Active Learning.

LPLgrad: Optimizing Active Learning Through Gradient Norm Sample Selection and Auxiliary Model Training A Comparative Survey of Deep Active Learning

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-12T16:22:47.837137Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9f3e8327-5ac8-44a8-a7f8-b0e5c0a4d5e7 · outbound

This paper cites A survey on deep active learning: Recent advances and new frontiers,.

LPLgrad: Optimizing Active Learning Through Gradient Norm Sample Selection and Auxiliary Model Training A survey on deep active learning: Recent advances and new frontiers,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-12T16:22:47.841667Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 16de7449-1393-4aa1-b0da-47a83181c24d · outbound

This paper cites FisherMask: Enhancing Neural Network Labeling Efficiency in Image Classification Using Fisher Information.

LPLgrad: Optimizing Active Learning Through Gradient Norm Sample Selection and Auxiliary Model Training FisherMask: Enhancing Neural Network Labeling Efficiency in Image Classification Using Fisher Information

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-08-12T16:22:47.984313Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3260333f-ddf8-4700-9dc0-76b0c20bced1 · outbound

This paper cites Verips: Verified pseudo- label selection for deep active learning,.

LPLgrad: Optimizing Active Learning Through Gradient Norm Sample Selection and Auxiliary Model Training Verips: Verified pseudo- label selection for deep active learning,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:22:48.172269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4678c88b-de1d-4254-baa4-1e3df709f128 · outbound

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

LPLgrad: Optimizing Active Learning Through Gradient Norm Sample Selection and Auxiliary Model Training A new active labeling method for deep learning,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-12T16:22:47.853369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8093aecc-e903-45db-bf83-dcf27943191b · outbound

This paper cites Reading digits in natural images with unsupervised feature learning,.

LPLgrad: Optimizing Active Learning Through Gradient Norm Sample Selection and Auxiliary Model Training Reading digits in natural images with unsupervised feature learning,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T16:22:47.857048Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4cee61d9-8b0f-45f3-9048-4d037fd1f7f9 · outbound

This paper cites Active Learning for Convolutional Neural Networks: A Core-Set Approach.

LPLgrad: Optimizing Active Learning Through Gradient Norm Sample Selection and Auxiliary Model Training Active Learning for Convolutional Neural Networks: A Core-Set Approach

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-12T16:22:47.861003Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 386af3f9-fc4a-422d-b354-027bdb420013 · outbound

This paper cites Variational adversarial active learning,.

LPLgrad: Optimizing Active Learning Through Gradient Norm Sample Selection and Auxiliary Model Training Variational adversarial active learning,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:22:48.146703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 170ec4e9-6122-42e0-9b2b-e1bb05ff6cda · outbound

This paper cites Deep active learning: Unified and principled method for query and training,.

LPLgrad: Optimizing Active Learning Through Gradient Norm Sample Selection and Auxiliary Model Training Deep active learning: Unified and principled method for query and training,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:22:48.135305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5ccf621f-edea-447e-848b-505b33a64304 · outbound

This paper cites Learning loss for active learning,.

LPLgrad: Optimizing Active Learning Through Gradient Norm Sample Selection and Auxiliary Model Training Learning loss for active learning,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-12T16:22:47.871067Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 181aa8f7-8928-4854-bfc8-917ab4ce707b · outbound

This paper cites Tiny imagenet visual recognition challenge,.

LPLgrad: Optimizing Active Learning Through Gradient Norm Sample Selection and Auxiliary Model Training Tiny imagenet visual recognition challenge,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T16:22:47.874581Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9257fada-8dc2-4200-86da-e57e511c329b · outbound

This paper cites EMNIST: an extension of MNIST to handwritten letters.

LPLgrad: Optimizing Active Learning Through Gradient Norm Sample Selection and Auxiliary Model Training EMNIST: an extension of MNIST to handwritten letters

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T16:22:47.877773Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 61572134-1d51-4a32-8fa2-76970b727bcd · outbound

This paper cites Boosting active learning via improving test performance,.

LPLgrad: Optimizing Active Learning Through Gradient Norm Sample Selection and Auxiliary Model Training Boosting active learning via improving test performance,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:22:48.112316Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f33a75da-9eb1-4b97-9b48-b5f3fd65dc0d · outbound

This paper cites Entropic open- set active learning,.

LPLgrad: Optimizing Active Learning Through Gradient Norm Sample Selection and Auxiliary Model Training Entropic open- set active learning,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:22:48.101978Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5420bd37-7c9c-4470-a766-265014c1ee45 · outbound

This paper cites Deep active learning with noise stability,.

LPLgrad: Optimizing Active Learning Through Gradient Norm Sample Selection and Auxiliary Model Training Deep active learning with noise stability,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:22:48.091664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 52c0d570-72c4-4497-a2e2-26317a6adc2b · outbound

This paper cites Active learning guided by efficient surrogate learners,.

LPLgrad: Optimizing Active Learning Through Gradient Norm Sample Selection and Auxiliary Model Training Active learning guided by efficient surrogate learners,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:22:48.081480Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c088d099-ce26-404a-8b98-758915328178 · outbound

This paper cites Cifar-10 (canadian institute for advanced research),.

LPLgrad: Optimizing Active Learning Through Gradient Norm Sample Selection and Auxiliary Model Training Cifar-10 (canadian institute for advanced research),

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-12T16:22:47.895098Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d5cd647d-15be-4ea1-96ea-29feb87c502e · outbound

This paper cites Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories,.

LPLgrad: Optimizing Active Learning Through Gradient Norm Sample Selection and Auxiliary Model Training Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:22:48.063736Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1071e67c-bd3b-4903-ab76-d787546d255c · outbound

This paper cites Disaster images dataset, version 1,.

LPLgrad: Optimizing Active Learning Through Gradient Norm Sample Selection and Auxiliary Model Training Disaster images dataset, version 1,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:22:48.051507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5c3536aa-0293-41af-bc0a-3fdf3b8f1af1 · outbound

This paper cites Latent structured active learning,.

LPLgrad: Optimizing Active Learning Through Gradient Norm Sample Selection and Auxiliary Model Training Latent structured active learning,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-12T16:22:47.904809Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3bf3a115-fbb8-47aa-8f4b-fdd735655ef0 · outbound

This paper cites The power of ensembles for active learning in image classification,.

LPLgrad: Optimizing Active Learning Through Gradient Norm Sample Selection and Auxiliary Model Training The power of ensembles for active learning in image classification,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-12T16:22:47.907979Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 52bc8b21-c21f-4ae3-9122-cf1d129f8394 · outbound

This paper cites Similar: Sub- modular information measures based active learning in realistic scenar- ios,.

LPLgrad: Optimizing Active Learning Through Gradient Norm Sample Selection and Auxiliary Model Training Similar: Sub- modular information measures based active learning in realistic scenar- ios,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:22:48.026956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ec760d9f-6822-406d-a06a-e02718d125b1 · outbound

This paper cites Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets.

LPLgrad: Optimizing Active Learning Through Gradient Norm Sample Selection and Auxiliary Model Training Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-12T16:22:47.914602Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a759149a-34b3-472b-a9d2-f256fe772015 · outbound

This paper cites Active learning under label shift,.

LPLgrad: Optimizing Active Learning Through Gradient Norm Sample Selection and Auxiliary Model Training Active learning under label shift,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:22:48.015659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d03754a1-447a-4842-b3df-4a8c2624dad6 · outbound

This paper cites Nearest neighbor classifier embedded network for active learning,.

LPLgrad: Optimizing Active Learning Through Gradient Norm Sample Selection and Auxiliary Model Training Nearest neighbor classifier embedded network for active learning,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:22:48.005284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Pith citing papers

No inbound Pith citation observations are available.