Paper Citation Record · LEDGER

Tricks and Plug-ins for Gradient Boosting in Image Classification

As of 19 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2507.22842.

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

pith.paper-citation-record.v1
2507.22842 v4

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T11:19:12.710982Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

27 of 27 outbound references displayed

verified exact2
verified fuzzy21
unresolved4
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fbb3d84e-bc25-4dbb-a428-30bb76c173ca · outbound

This paper cites Deep residual learning for image recognition,.

Tricks and Plug-ins for Gradient Boosting in Image Classification Deep residual learning for image recognition,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T11:19:09.473011Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e90669cd-f45a-43fc-b526-a85594d64eab · outbound

This paper cites Imagenet classification with deep convolutional neural networks,.

Tricks and Plug-ins for Gradient Boosting in Image Classification Imagenet classification with deep convolutional neural networks,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:19:17.485451Z

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.

Observation b5a28f00-5104-4bce-941d-a9c71c7a451a · outbound

This paper cites Bilinear CNN models for fine-grained visual recognition,.

Tricks and Plug-ins for Gradient Boosting in Image Classification Bilinear CNN models for fine-grained visual recognition,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:19:17.281761Z

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.

Observation 1d34e98f-2297-4317-a4f4-d602e091ce2e · outbound

This paper cites Rich feature hierarchies for accurate object detection and semantic segmentation,.

Tricks and Plug-ins for Gradient Boosting in Image Classification Rich feature hierarchies for accurate object detection and semantic segmentation,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:19:17.061218Z

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.

Observation 5fd23d12-63b9-4c7d-94f6-2b5bcf9f7186 · outbound

This paper cites DenseNet: Implementing Efficient ConvNet Descriptor Pyramids.

Tricks and Plug-ins for Gradient Boosting in Image Classification DenseNet: Implementing Efficient ConvNet Descriptor Pyramids

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T11:19:09.913911Z

Source-reported events for the cited work

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Observation 43d3760e-2fb2-4880-ac96-8b375f682572 · outbound

This paper cites Faster R-CNN: Towards real- time object detection with region proposal networks,.

Tricks and Plug-ins for Gradient Boosting in Image Classification Faster R-CNN: Towards real- time object detection with region proposal networks,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:19:16.926138Z

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.

Observation 39fbb3d0-84a5-443d-9e0d-a039776ae6ae · outbound

This paper cites AMC: AutoML for model compression and acceleration on mobile devices,.

Tricks and Plug-ins for Gradient Boosting in Image Classification AMC: AutoML for model compression and acceleration on mobile devices,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:19:16.756324Z

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.

Observation bd7e9b88-75dc-470b-8c72-857fa880f57c · outbound

This paper cites Evolutionary neural AutoML for deep learning,.

Tricks and Plug-ins for Gradient Boosting in Image Classification Evolutionary neural AutoML for deep learning,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:19:16.549083Z

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.

Observation caa8fc90-40d1-44ae-bb72-ace690d98824 · outbound

This paper cites Induction of decision trees,.

Tricks and Plug-ins for Gradient Boosting in Image Classification Induction of decision trees,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:19:16.451968Z

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.

Observation 4576d2a6-d909-432d-87a7-d7ae7aeb680c · outbound

This paper cites Boosted convolutional neural networks,.

Tricks and Plug-ins for Gradient Boosting in Image Classification Boosted convolutional neural networks,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:19:16.305853Z

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.

Observation 21faeb62-4d72-4f0d-bdd1-a6370af30862 · outbound

This paper cites Boosted convolutional neural network for object recognition at large scale,.

Tricks and Plug-ins for Gradient Boosting in Image Classification Boosted convolutional neural network for object recognition at large scale,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:19:16.103287Z

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.

Observation 2cb52ae9-0fd5-495d-bf8e-c8aee1bc1e63 · outbound

This paper cites Boosted Training of Convolutional Neural Networks for Multi-Class Segmentation.

Tricks and Plug-ins for Gradient Boosting in Image Classification Boosted Training of Convolutional Neural Networks for Multi-Class Segmentation

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-06T11:19:13.175969Z

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.

Observation 2d5e33c0-f678-4927-931d-a1de8283f1bd · outbound

This paper cites Image classification based on the boost convolutional neural network,.

Tricks and Plug-ins for Gradient Boosting in Image Classification Image classification based on the boost convolutional neural network,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:19:15.957933Z

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.

Observation 786dcca3-d6b5-4742-8aa8-6eb52f7aeb65 · outbound

This paper cites Incremental boosting convolutional neural network for facial action unit recognition,.

Tricks and Plug-ins for Gradient Boosting in Image Classification Incremental boosting convolutional neural network for facial action unit recognition,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:19:15.748665Z

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.

Observation cb82a2b2-2b53-4612-ac8a-f1c572262880 · outbound

This paper cites Gradient boosting machine and object-based cnn for land cover classification,.

Tricks and Plug-ins for Gradient Boosting in Image Classification Gradient boosting machine and object-based cnn for land cover classification,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:19:15.584107Z

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.

Observation 2c267af9-8bd1-40ec-8e8a-3bba8697c80c · outbound

This paper cites A gradient boosting approach for training convolutional and deep neural networks,.

Tricks and Plug-ins for Gradient Boosting in Image Classification A gradient boosting approach for training convolutional and deep neural networks,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:19:15.348139Z

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.

Observation 09e08b3a-ac6a-4ebd-9607-b0ef70094148 · outbound

This paper cites Stochastic gradient descent, weighted sampling, and the randomized Kaczmarz algorithm,.

Tricks and Plug-ins for Gradient Boosting in Image Classification Stochastic gradient descent, weighted sampling, and the randomized Kaczmarz algorithm,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:19:15.104396Z

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.

Observation fce5eda4-66d8-4296-be27-a1a8a90520de · outbound

This paper cites Stochastic optimization with importance sam- pling for regularized loss minimization,.

Tricks and Plug-ins for Gradient Boosting in Image Classification Stochastic optimization with importance sam- pling for regularized loss minimization,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:19:14.838441Z

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.

Observation 655b73ae-c6c9-4314-8a1b-1a447a12c8bb · outbound

This paper cites Not All Samples Are Created Equal: Deep Learning with Importance Sampling.

Tricks and Plug-ins for Gradient Boosting in Image Classification Not All Samples Are Created Equal: Deep Learning with Importance Sampling

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T11:19:11.819606Z

Source-reported events for the cited work

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Observation 6e85fd8b-60e7-4387-967c-fdd778ad21fc · outbound

This paper cites Importance Sampling for Minibatches.

Tricks and Plug-ins for Gradient Boosting in Image Classification Importance Sampling for Minibatches

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-06T11:19:12.957302Z

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.

Observation d82ae604-cc51-48fe-82d6-a4f7c2a86e4d · outbound

This paper cites Multi-class AdaBoost,.

Tricks and Plug-ins for Gradient Boosting in Image Classification Multi-class AdaBoost,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:19:14.711594Z

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.

Observation 88afbff5-cf51-41c5-95af-cb7ce0f3b23a · outbound

This paper cites A theory of multiclass boosting,.

Tricks and Plug-ins for Gradient Boosting in Image Classification A theory of multiclass boosting,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:19:14.474501Z

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.

Observation aa4bfdc7-c1e6-42a9-b8c2-cd7269fbce6d · outbound

This paper cites Multiclass Boosting: Theory and algorithms,.

Tricks and Plug-ins for Gradient Boosting in Image Classification Multiclass Boosting: Theory and algorithms,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:19:14.185283Z

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.

Observation 88edbbf2-059b-4c87-a67a-8552b35ef47c · outbound

This paper cites Automatic differentiation in pytorch,.

Tricks and Plug-ins for Gradient Boosting in Image Classification Automatic differentiation in pytorch,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:19:13.878359Z

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.

Observation 7a2bac16-433d-4fc8-a89a-4a23dd7062f7 · outbound

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

Tricks and Plug-ins for Gradient Boosting in Image Classification Learning multiple layers of features from tiny images,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:19:13.677297Z

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.

Observation 453c3828-883c-461e-97b3-12c109663876 · outbound

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

Tricks and Plug-ins for Gradient Boosting in Image Classification Reading digits in natural images with unsupervised feature learning,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:19:13.479966Z

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.

Observation 47a25f99-53dc-4261-9be3-8b7c82c9a7cf · outbound

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

Tricks and Plug-ins for Gradient Boosting in Image Classification ImageNet: A large-scale hierarchical image database,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T11:19:12.710982Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.