Paper Citation Record · LEDGER

How Effective Can Dropout Be in Multiple Instance Learning ?

As of 20 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2504.14783.

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

pith.paper-citation-record.v1
2504.14783 v2

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:45:00.918793Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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

47 of 47 outbound references displayed

verified exact0
verified fuzzy25
unresolved22
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d304cc66-5731-44c3-9bed-48e42cf76c68 · outbound

This paper cites write newline.

How Effective Can Dropout Be in Multiple Instance Learning ? write newline

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-16T11:45:00.752813Z

Source-reported events for the cited work

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Observation a067d847-caea-4a5c-b1a6-db3c01c7d04f · outbound

This paper cites Support vector machines for multiple-instance learning.

How Effective Can Dropout Be in Multiple Instance Learning ? Support vector machines for multiple-instance learning

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:45:01.392378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 16c2e4a4-3d7f-4096-9e63-b0aed2aadd6e · outbound

This paper cites Robust object tracking with online multiple instance learning.

How Effective Can Dropout Be in Multiple Instance Learning ? Robust object tracking with online multiple instance learning

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:45:01.382034Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d43c5f72-dc18-42f5-82a6-d4fc40486704 · outbound

This paper cites J., Ding, T., Lu, M.

How Effective Can Dropout Be in Multiple Instance Learning ? J., Ding, T., Lu, M

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:45:01.370817Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2681ed58-c73a-4269-9cab-d313d77f1ce1 · outbound

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

How Effective Can Dropout Be in Multiple Instance Learning ? A simple framework for contrastive learning of visual representations

Reference 5

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Observation 9645f36a-e24a-425a-95b8-d78c32c10d11 · outbound

This paper cites Timemil: advancing multivariate time series classification via a time-aware multiple instance learning.

How Effective Can Dropout Be in Multiple Instance Learning ? Timemil: advancing multivariate time series classification via a time-aware multiple instance learning

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:45:01.354924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation bf275059-c03b-4ff6-8e96-633b050cb16e · outbound

This paper cites and Shim, H.

How Effective Can Dropout Be in Multiple Instance Learning ? and Shim, H

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:45:01.344111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3021d733-d9be-4d9a-af68-d0998a9e3adb · outbound

This paper cites G., Lathrop, R.

How Effective Can Dropout Be in Multiple Instance Learning ? G., Lathrop, R

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:45:01.333446Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation aa8176f5-bbca-4edb-b922-634fe1b326ef · outbound

This paper cites Sharp minima can generalize for deep nets.

How Effective Can Dropout Be in Multiple Instance Learning ? Sharp minima can generalize for deep nets

Reference 9

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Source-reported events for the cited work

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Observation 6a5bf9f4-838c-4132-bc02-7e09d0a3ab0f · outbound

This paper cites Inherently Interpretable Time Series Classification via Multiple Instance Learning.

How Effective Can Dropout Be in Multiple Instance Learning ? Inherently Interpretable Time Series Classification via Multiple Instance Learning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-16T11:45:00.787667Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 55343bd3-10ed-434c-8d28-9e5e2581edd9 · outbound

This paper cites and Zhou, Z.-H.

How Effective Can Dropout Be in Multiple Instance Learning ? and Zhou, Z.-H

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:45:01.316509Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8a4ef700-828f-4764-a175-a87be6143c91 · outbound

This paper cites Sharpness-aware minimization for efficiently improving generalization.

How Effective Can Dropout Be in Multiple Instance Learning ? Sharpness-aware minimization for efficiently improving generalization

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-16T11:45:00.794992Z

Source-reported events for the cited work

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Observation 57155dfd-b364-4a20-9f12-c55d116921c7 · outbound

This paper cites an unresolved cited work.

How Effective Can Dropout Be in Multiple Instance Learning ? Unresolved cited work

Reference 13

Resolution: unresolved; raw_fallback, observed 2026-08-16T11:45:01.300040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 64589b45-22c3-4842-83d0-2808951b4bd6 · outbound

This paper cites Improving neural networks by preventing co-adaptation of feature detectors.

How Effective Can Dropout Be in Multiple Instance Learning ? Improving neural networks by preventing co-adaptation of feature detectors

Reference 14

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Source-reported events for the cited work

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Observation 194cd80b-d431-47c6-9548-9574f104b095 · outbound

This paper cites M., Gao, Y., Davis, J.

How Effective Can Dropout Be in Multiple Instance Learning ? M., Gao, Y., Davis, J

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:45:01.289370Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 41b84b3d-06d2-4c99-b616-7a235bd8be55 · outbound

This paper cites Unleash the power of state space model for whole slide image with local aware scanning and importance resampling.

How Effective Can Dropout Be in Multiple Instance Learning ? Unleash the power of state space model for whole slide image with local aware scanning and importance resampling

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:45:01.278411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2555d08e-9a23-418a-995a-34ce107b15d4 · outbound

This paper cites Attention-based deep multiple instance learning.

How Effective Can Dropout Be in Multiple Instance Learning ? Attention-based deep multiple instance learning

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-16T11:45:00.811676Z

Source-reported events for the cited work

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Observation f4de8a5b-095a-4ad5-a9e3-6b62b49582bf · outbound

This paper cites The Break-Even Point on Optimization Trajectories of Deep Neural Networks.

How Effective Can Dropout Be in Multiple Instance Learning ? The Break-Even Point on Optimization Trajectories of Deep Neural Networks

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-16T11:45:00.815016Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation db46e1f4-15d8-46ca-9494-19c2acee15ec · outbound

This paper cites J., Williamson, D.

How Effective Can Dropout Be in Multiple Instance Learning ? J., Williamson, D

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:45:01.260049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 96d62a85-4e34-4876-8ae9-6883fa9ba5cb · outbound

This paper cites T., and Cevher, V.

How Effective Can Dropout Be in Multiple Instance Learning ? T., and Cevher, V

Reference 20

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Source-reported events for the cited work

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Observation 178bf4eb-b249-453c-8113-0800ad747ecd · outbound

This paper cites On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima.

How Effective Can Dropout Be in Multiple Instance Learning ? On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima

Reference 21

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Source-reported events for the cited work

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How Effective Can Dropout Be in Multiple Instance Learning ? Unresolved cited work

Reference 22

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 72093af0-9fef-4721-a548-b90689746c8f · outbound

This paper cites Dropout reduces underfitting.

How Effective Can Dropout Be in Multiple Instance Learning ? Dropout reduces underfitting

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:45:01.231395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 49fe6840-d700-43b2-be24-117d0bd3e445 · outbound

This paper cites BAM: Bottleneck Attention Module.

How Effective Can Dropout Be in Multiple Instance Learning ? BAM: Bottleneck Attention Module

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-16T11:45:00.835113Z

Source-reported events for the cited work

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Observation e06a1291-ba77-47d9-b51f-fae026d56b66 · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library.

How Effective Can Dropout Be in Multiple Instance Learning ? Pytorch: An imperative style, high-performance deep learning library

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-16T11:45:00.838793Z

Source-reported events for the cited work

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Observation 4b204e66-6827-4055-a51a-42e8293d0807 · outbound

This paper cites SC-MIL: Sparsely Coded Multiple Instance Learning for Whole Slide Image Classification.

How Effective Can Dropout Be in Multiple Instance Learning ? SC-MIL: Sparsely Coded Multiple Instance Learning for Whole Slide Image Classification

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-16T11:45:00.841981Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ff28f06a-d0e0-4836-8550-afbd18bcd35f · outbound

This paper cites Boosting whole slide image classification from the perspectives of distribution, correlation and magnification.

How Effective Can Dropout Be in Multiple Instance Learning ? Boosting whole slide image classification from the perspectives of distribution, correlation and magnification

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:45:01.213117Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 60daa06f-635f-47f1-a986-4b0db5d278e2 · outbound

This paper cites Multiple-instance learning for medical image and video analysis.

How Effective Can Dropout Be in Multiple Instance Learning ? Multiple-instance learning for medical image and video analysis

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:45:01.202449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 01bee578-7a7d-44c3-a9fa-1a4fd07ac263 · outbound

This paper cites Transmil: Transformer based correlated multiple instance learning for whole slide image classification.

How Effective Can Dropout Be in Multiple Instance Learning ? Transmil: Transformer based correlated multiple instance learning for whole slide image classification

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:45:01.191799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 585640cd-6d7d-4edc-bc1b-bbec02ddc391 · outbound

This paper cites Multimodal Prototyping for cancer survival prediction.

How Effective Can Dropout Be in Multiple Instance Learning ? Multimodal Prototyping for cancer survival prediction

Reference 30

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Source-reported events for the cited work

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Observation 5006ca9d-578b-4fb1-b8e4-01a537bc2dc2 · outbound

This paper cites Dropout: A simple way to prevent neural networks from overfitting.

How Effective Can Dropout Be in Multiple Instance Learning ? Dropout: A simple way to prevent neural networks from overfitting

Reference 31

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Source-reported events for the cited work

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Observation e46fbae2-08e9-4e7c-b768-0499684b7e17 · outbound

This paper cites Multiple instance learning framework with masked hard instance mining for whole slide image classification.

How Effective Can Dropout Be in Multiple Instance Learning ? Multiple instance learning framework with masked hard instance mining for whole slide image classification

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:45:01.174831Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d5006f1e-8759-4fd4-b72e-813d877ecc68 · outbound

This paper cites Efficient object localization using convolutional networks.

How Effective Can Dropout Be in Multiple Instance Learning ? Efficient object localization using convolutional networks

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:45:01.164332Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a6fcee20-5b1f-4d07-be2b-3db5e2c06c1c · outbound

This paper cites Multiple instance learning with graph neural networks.

How Effective Can Dropout Be in Multiple Instance Learning ? Multiple instance learning with graph neural networks

Reference 35

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Observation 0a797c4c-f19d-4974-8638-020eec880246 · outbound

This paper cites Non-local neural networks.

How Effective Can Dropout Be in Multiple Instance Learning ? Non-local neural networks

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:45:01.153778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0b304c3a-a71f-4507-b3d2-2ba07037a80c · outbound

This paper cites Revisiting multiple instance neural networks.

How Effective Can Dropout Be in Multiple Instance Learning ? Revisiting multiple instance neural networks

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:45:01.142341Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 810d1363-1e09-47c2-8c3b-1f586052eac9 · outbound

This paper cites an unresolved cited work.

How Effective Can Dropout Be in Multiple Instance Learning ? Unresolved cited work

Reference 38

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Source-reported events for the cited work

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Observation f2a2ec02-ace6-471a-8bf2-30b9f7b22105 · outbound

This paper cites and Zhang, J.

How Effective Can Dropout Be in Multiple Instance Learning ? and Zhang, J

Reference 39

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Source-reported events for the cited work

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Observation c58d8fc6-50e0-401f-b8a9-04314636ec79 · outbound

This paper cites Camel: A weakly supervised learning framework for histopathology image segmentation.

How Effective Can Dropout Be in Multiple Instance Learning ? Camel: A weakly supervised learning framework for histopathology image segmentation

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:45:01.118168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 84a97785-550d-4b54-855f-75b9c718fa70 · outbound

This paper cites Deep multi-instance learning with dynamic pooling.

How Effective Can Dropout Be in Multiple Instance Learning ? Deep multi-instance learning with dynamic pooling

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:45:01.106831Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d1aebfda-1add-42ec-a00c-3fa536872d4d · outbound

This paper cites E., and Zheng, Y.

How Effective Can Dropout Be in Multiple Instance Learning ? E., and Zheng, Y

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:45:01.094966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 64e7fe5a-d089-40df-9d8b-3a68ea87bd62 · outbound

This paper cites and Xu, Z.-Q.

How Effective Can Dropout Be in Multiple Instance Learning ? and Xu, Z.-Q

Reference 43

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Source-reported events for the cited work

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Observation c823109c-957f-40db-98c2-4d37c34c055e · outbound

This paper cites Dynamic policy-driven adaptive multi-instance learning for whole slide image classification.

How Effective Can Dropout Be in Multiple Instance Learning ? Dynamic policy-driven adaptive multi-instance learning for whole slide image classification

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:45:01.078051Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 96b8fa58-be2b-4c2a-a118-714054e86336 · outbound

This paper cites PDL: Regularizing Multiple Instance Learning with Progressive Dropout Layers.

How Effective Can Dropout Be in Multiple Instance Learning ? PDL: Regularizing Multiple Instance Learning with Progressive Dropout Layers

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-16T11:45:00.908193Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f269c9d-a7e2-4f82-8695-e87c798bf342 · outbound

This paper cites M., and Wang, Y.

How Effective Can Dropout Be in Multiple Instance Learning ? M., and Wang, Y

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:45:01.066958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 01d13ca4-38c2-40d7-943f-a9944906ab37 · outbound

This paper cites Dgr-mil: Exploring diverse global representation in multiple instance learning for whole slide image classification.

How Effective Can Dropout Be in Multiple Instance Learning ? Dgr-mil: Exploring diverse global representation in multiple instance learning for whole slide image classification

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:45:01.056067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation bb9b01af-2625-44e0-b9df-09adfdd1f533 · outbound

This paper cites Asymmetric non-local neural networks for semantic segmentation.

How Effective Can Dropout Be in Multiple Instance Learning ? Asymmetric non-local neural networks for semantic segmentation

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:45:01.044973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

No inbound Pith citation observations are available.