Paper Citation Record · LEDGER

Learning from Label Proportions with Generative Adversarial Networks

As of 19 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:1909.02180.

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

pith.paper-citation-record.v1
1909.02180 v4

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T05:06:30.971642Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

34 of 34 outbound references displayed

verified exact0
verified fuzzy23
unresolved11
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malformed identifier0
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External citation measurements

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Outbound references

Observation 5fbda7f7-17c9-41a1-a13f-49b03b09fc0b · outbound

This paper cites Ardehaly and Aron Culotta.

Learning from Label Proportions with Generative Adversarial Networks Ardehaly and Aron Culotta

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:06:31.520057Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e01283b4-ec54-4c9d-ac38-33dbcb060087 · outbound

This paper cites Towards principled methods for training generative adver- sarial networks.

Learning from Label Proportions with Generative Adversarial Networks Towards principled methods for training generative adver- sarial networks

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:06:31.506459Z

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Observation a8bc6da6-ed41-44ec-892a-4cbf0694ef2d · outbound

This paper cites Wasserstein generative adversarial networks.

Learning from Label Proportions with Generative Adversarial Networks Wasserstein generative adversarial networks

Reference 3

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Observation 547daeb3-f619-4b15-853e-7a1a7960a183 · outbound

This paper cites Relational inductive biases, deep learning, and graph networks.

Learning from Label Proportions with Generative Adversarial Networks Relational inductive biases, deep learning, and graph networks

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-14T05:06:30.846879Z

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Unavailable: canonical work link unavailable.

Observation 377f1246-0bf2-4c08-95ff-aff76420088e · outbound

This paper cites Representation learning: A review and new perspectives.

Learning from Label Proportions with Generative Adversarial Networks Representation learning: A review and new perspectives

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-14T05:06:30.851908Z

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Observation 02c474a8-1857-4ee4-a4ba-2cff77f0f000 · outbound

This paper cites Pattern Recognition and Machine Learning.

Learning from Label Proportions with Generative Adversarial Networks Pattern Recognition and Machine Learning

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:06:31.475913Z

Source-reported events for the cited work

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Observation af1342e6-6ef0-438a-bc4d-6404cb7f410f · outbound

This paper cites Convex optimization.

Learning from Label Proportions with Generative Adversarial Networks Convex optimization

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-14T05:06:30.861148Z

Source-reported events for the cited work

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Observation 49106635-7395-4035-80de-bd876d18ff8b · outbound

This paper cites Good semi-supervised learning that requires a bad gan.

Learning from Label Proportions with Generative Adversarial Networks Good semi-supervised learning that requires a bad gan

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:06:31.453702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e16f5c7f-ecb5-4124-8562-7afa3525250c · outbound

This paper cites Dietterich, Richard H.

Learning from Label Proportions with Generative Adversarial Networks Dietterich, Richard H

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:06:31.440240Z

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Observation 00e8b16a-91fd-4e10-bf75-c13cfe7b6740 · outbound

This paper cites Deep multi-class learning from label proportions.

Learning from Label Proportions with Generative Adversarial Networks Deep multi-class learning from label proportions

Reference 10

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Source-reported events for the cited work

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Observation a550f698-b7cf-43fa-ae05-db9e711d1ab2 · outbound

This paper cites Generative adversarial nets.

Learning from Label Proportions with Generative Adversarial Networks Generative adversarial nets

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:06:31.426848Z

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Observation 3eca4d6d-09b1-434e-853c-55aa943a3bdd · outbound

This paper cites Semi-supervised learning by entropy minimization.

Learning from Label Proportions with Generative Adversarial Networks Semi-supervised learning by entropy minimization

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-14T05:06:30.881983Z

Source-reported events for the cited work

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Observation 0654713a-bbac-4ea5-9528-253ccb25158f · outbound

This paper cites Deep residual learning for image recognition.

Learning from Label Proportions with Generative Adversarial Networks Deep residual learning for image recognition

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:06:31.401866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 426f85e2-848e-4933-85a7-ff3808de5a1f · outbound

This paper cites Deep neural networks for acoustic modeling in speech recognition.

Learning from Label Proportions with Generative Adversarial Networks Deep neural networks for acoustic modeling in speech recognition

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:06:31.344163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0beae9a7-bde8-43f1-9ddf-f0176e75c6bc · outbound

This paper cites Perceptual losses for real-time style transfer and super-resolution.

Learning from Label Proportions with Generative Adversarial Networks Perceptual losses for real-time style transfer and super-resolution

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:06:31.296900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fbfc5853-fcf2-4fff-8b6a-f7d993826c90 · outbound

This paper cites Auto-Encoding Variational Bayes.

Learning from Label Proportions with Generative Adversarial Networks Auto-Encoding Variational Bayes

Reference 16

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Source-reported events for the cited work

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Observation 1692ceeb-5ad9-430a-90fd-ea4317b44909 · outbound

This paper cites Semi-supervised learning with deep generative models.

Learning from Label Proportions with Generative Adversarial Networks Semi-supervised learning with deep generative models

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:06:31.282158Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 68b2ba45-61a7-4a85-a093-c34891f5a0e4 · outbound

This paper cites Network In Network.

Learning from Label Proportions with Generative Adversarial Networks Network In Network

Reference 18

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Source-reported events for the cited work

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Observation 29a66af3-6956-4135-bf95-f8943504867f · outbound

This paper cites A framework for multiple-instance learning.

Learning from Label Proportions with Generative Adversarial Networks A framework for multiple-instance learning

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:06:31.267575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9a30bdd6-25e6-4529-8434-0a7a0cb5b2d6 · outbound

This paper cites The expectation-maximization algorithm.

Learning from Label Proportions with Generative Adversarial Networks The expectation-maximization algorithm

Reference 20

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Source-reported events for the cited work

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Observation 8e6984f3-7f98-4703-9ef3-90bc795bbbec · outbound

This paper cites (Almost) no label no cry.

Learning from Label Proportions with Generative Adversarial Networks (Almost) no label no cry

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:06:31.244791Z

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Observation 32f6d559-de8b-4160-bced-a6d338aecaaa · outbound

This paper cites Learning with label proportions via NPSVM.

Learning from Label Proportions with Generative Adversarial Networks Learning with label proportions via NPSVM

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:06:31.230381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f54200e2-6066-4149-ae51-904498635b97 · outbound

This paper cites Smola, Tiberio S.

Learning from Label Proportions with Generative Adversarial Networks Smola, Tiberio S

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:06:31.216255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8ac30876-09b9-4e62-b31f-db67775e57c0 · outbound

This paper cites Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks.

Learning from Label Proportions with Generative Adversarial Networks Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-14T05:06:30.931047Z

Source-reported events for the cited work

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Observation 3ff3d659-efb0-4255-b33c-cf2eae662636 · outbound

This paper cites Semi- supervised learning with ladder networks.

Learning from Label Proportions with Generative Adversarial Networks Semi- supervised learning with ladder networks

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:06:31.202476Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b30db5dc-955d-45b5-8c00-c590d9129c8b · outbound

This paper cites You only look once: Uniﬁed, real-time object detection.

Learning from Label Proportions with Generative Adversarial Networks You only look once: Uniﬁed, real-time object detection

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:06:31.189005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2bf9b29e-f8d1-4755-b26c-5a97abd703a0 · outbound

This paper cites SVM classiﬁer estimation from group probabilities.

Learning from Label Proportions with Generative Adversarial Networks SVM classiﬁer estimation from group probabilities

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:06:31.175176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ad8d3291-b6c7-4835-ab57-2936450c6f16 · outbound

This paper cites Improved techniques for training GANs.

Learning from Label Proportions with Generative Adversarial Networks Improved techniques for training GANs

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:06:31.160414Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 26dc623b-1c93-4c6f-afd7-62e303eae335 · outbound

This paper cites Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks.

Learning from Label Proportions with Generative Adversarial Networks Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-14T05:06:30.951585Z

Source-reported events for the cited work

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Observation d423ce61-ea37-4758-9d83-a7ad2fc20368 · outbound

This paper cites Rethinking the inception architecture for computer vision.

Learning from Label Proportions with Generative Adversarial Networks Rethinking the inception architecture for computer vision

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:06:31.144528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 63fb1662-744f-4a72-926f-e469e960275e · outbound

This paper cites Multi-class learning from class proportions.

Learning from Label Proportions with Generative Adversarial Networks Multi-class learning from class proportions

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:06:31.130360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 42907df0-57ee-4c53-a4a5-f52e6ade73cb · outbound

This paper cites Adversarial perturbations of deep neural networks.

Learning from Label Proportions with Generative Adversarial Networks Adversarial perturbations of deep neural networks

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:06:31.116334Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 70c9efc7-349a-431d-8118-d533727f3dff · outbound

This paper cites Yu, Liangliang Cao, Michele Merler, et al.

Learning from Label Proportions with Generative Adversarial Networks Yu, Liangliang Cao, Michele Merler, et al

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:06:31.102337Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b669598f-307e-4226-ae22-89a2c1ec425c · outbound

This paper cites Yu, Dong Liu, Sanjiv Kumar, et al.∝-SVM for learning with label proportions.

Learning from Label Proportions with Generative Adversarial Networks Yu, Dong Liu, Sanjiv Kumar, et al.∝-SVM for learning with label proportions

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:06:31.087410Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

No inbound Pith citation observations are available.