Paper Citation Record · LEDGER

ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization

As of 17 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 1 inbound Pith citation observation for arXiv:2412.01476.

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

pith.paper-citation-record.v1
2412.01476 v2

Coverage vector

measured 49 of 49 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T04:23:00.476134Z

measured 50 of 50 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T04:23:00.476134Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-12T04:23:00.508923Z

Reference resolution

49 of 49 outbound references displayed

verified exact2
verified fuzzy26
unresolved20
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 05f90d2b-ea73-4b42-92ae-9056124c5904 · outbound

This paper cites Adaptive consistency regular- ization for semi-supervised transfer learning.

ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization Adaptive consistency regular- ization for semi-supervised transfer learning

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:23:01.104581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a1e3fe7b-945c-4a91-8acf-07d43eba513e · outbound

This paper cites The effects of adding noise during backprop- agation training on a generalization performance.

ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization The effects of adding noise during backprop- agation training on a generalization performance

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:23:01.093650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 957afca1-3380-4f33-81c8-79901e6d0415 · outbound

This paper cites A closer look at memorization in deep networks.

ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization A closer look at memorization in deep networks

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:23:01.079571Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2610edfe-987e-4339-8cce-45e000e0f689 · outbound

This paper cites Inte- grating structured biological data by kernel maximum mean discrepancy.

ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization Inte- grating structured biological data by kernel maximum mean discrepancy

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:23:01.062110Z

Source-reported events for the cited work

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Observation 28b2687f-8fb5-4411-acf5-cf07bdcedfab · outbound

This paper cites Understanding and utilizing deep neural networks trained with noisy labels.

ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization Understanding and utilizing deep neural networks trained with noisy labels

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:23:01.046576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation fc5bc05a-92ec-46af-b716-086176d1634a · outbound

This paper cites Sample prior guided robust model learning to suppress noisy labels.

ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization Sample prior guided robust model learning to suppress noisy labels

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:23:01.031387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4dc211d0-2bc9-4ebe-b8cc-9511b131ecb6 · outbound

This paper cites Reducing Transformer Depth on Demand with Structured Dropout.

ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization Reducing Transformer Depth on Demand with Structured Dropout

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-12T04:23:00.304717Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a1c0aaf1-01fe-456b-ade4-2b4cf9d9214a · outbound

This paper cites Unsupervised domain adaptation by backpropagation.

ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization Unsupervised domain adaptation by backpropagation

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:23:01.017507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3acbaa6d-29a7-40a2-af38-4e84037f43e1 · outbound

This paper cites Regu- larization theory and neural networks architectures.

ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization Regu- larization theory and neural networks architectures

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:23:01.004941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e807949b-a6b1-4314-97c1-42424faddd9a · outbound

This paper cites Connecting the dots with landmarks: Discriminatively learning domain- invariant features for unsupervised domain adaptation.

ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization Connecting the dots with landmarks: Discriminatively learning domain- invariant features for unsupervised domain adaptation

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:23:00.992170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8b6be91a-0084-414e-ac36-4457a82ef966 · outbound

This paper cites Generative adversarial nets.

ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization Generative adversarial nets

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T04:23:00.320169Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56fe05d4-dbff-470e-900c-94d2f8b549c8 · outbound

This paper cites Natural adversarial examples.

ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization Natural adversarial examples

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:23:00.971641Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 759b4bb3-2ca6-4aa1-aad2-52c8ba07fa3f · outbound

This paper cites Data augmentation instead of explicit regularization.

ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization Data augmentation instead of explicit regularization

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-12T04:23:00.327607Z

Source-reported events for the cited work

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Observation b334aefc-0860-4ea8-b6e8-bf2e8a4b9097 · outbound

This paper cites FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation.

ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-12T04:23:00.332216Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ce8cf5b-9496-46f9-b2b6-1091c2fc05e9 · outbound

This paper cites Searching for mo- bilenetv3.

ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization Searching for mo- bilenetv3

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:23:00.957489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3ccd0530-4d6b-452e-95a4-ea783d5e5650 · outbound

This paper cites Deep networks with stochastic depth.

ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization Deep networks with stochastic depth

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:23:00.945903Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e798f247-24fe-467f-a432-605f98f2abed · outbound

This paper cites Correcting sample selection bias by unlabeled data.

ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization Correcting sample selection bias by unlabeled data

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:23:00.934003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation aa35053a-7732-436d-8019-e676551c6d13 · outbound

This paper cites Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.

ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-12T04:23:00.349426Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dd3e8514-a778-431c-9e91-df727ce0b973 · outbound

This paper cites Image-to-image translation with conditional adversarial net- works.

ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization Image-to-image translation with conditional adversarial net- works

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:23:00.923099Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 74cd1f3d-0a90-4b13-86ab-a90b6ee93c81 · outbound

This paper cites Adversar- ial adaptation of synthetic or stale data.

ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization Adversar- ial adaptation of synthetic or stale data

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:23:00.911925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 483f4431-b4b9-4938-b882-3af2b6b0d059 · outbound

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

ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization Learning multiple layers of features from tiny images

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-12T04:23:00.363300Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fb583f0b-ee01-41b7-91ae-904b92af501d · outbound

This paper cites Regularization for Deep Learning: A Taxonomy.

ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization Regularization for Deep Learning: A Taxonomy

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-12T04:23:00.366800Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ff901887-23be-4ede-a616-c5a18273e135 · outbound

This paper cites Generalization and network design strategies.

ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization Generalization and network design strategies

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:23:00.896584Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 42d8b9bc-9f67-45ef-aedb-c9318a36b626 · outbound

This paper cites Layer normalization.

ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization Layer normalization

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:23:00.886198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e55022b1-f5b4-4317-b8b4-430940ecda82 · outbound

This paper cites DivideMix: Learning with Noisy Labels as Semi-supervised Learning.

ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-12T04:23:00.376508Z

Source-reported events for the cited work

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Observation a87d2b2b-65c7-449f-bc7a-93c1dc5b31d5 · outbound

This paper cites WebVision Database: Visual Learning and Understanding from Web Data.

ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization WebVision Database: Visual Learning and Understanding from Web Data

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-12T04:23:00.380243Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce56fa87-ff0d-4bd3-9e13-8937cb0c1dd2 · outbound

This paper cites A convnet for the 2020s.

ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization A convnet for the 2020s

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-12T04:23:00.384091Z

Source-reported events for the cited work

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Observation dbc631fb-dfc2-4b7a-9b54-d82b71676818 · outbound

This paper cites Revisiting Activation Regularization for Language RNNs.

ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization Revisiting Activation Regularization for Language RNNs

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-08-12T04:23:00.615082Z

Source-reported events for the cited work

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Observation 25d9e110-569a-447a-9466-9223eb26ae47 · outbound

This paper cites Which training methods for gans do actually converge? In International conference on machine learning, pages 3481–.

ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization Which training methods for gans do actually converge? In International conference on machine learning, pages 3481–

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-12T04:23:00.391354Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1d4e33f3-a346-4d09-93af-3c5123f2201c · outbound

This paper cites Unrolled Generative Adversarial Networks.

ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization Unrolled Generative Adversarial Networks

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-12T04:23:00.395175Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b365c0d-cea7-410c-ba60-b9e0089097c3 · outbound

This paper cites When does label smoothing help? Advances in neural in- formation processing systems, 32, 2019.

ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization When does label smoothing help? Advances in neural in- formation processing systems, 32, 2019

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-12T04:23:00.398874Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cb284c42-3bd7-41a4-95d0-1c00bd7300f3 · outbound

This paper cites Automated flower classification over a large number of classes.

ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization Automated flower classification over a large number of classes

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-12T04:23:00.402587Z

Source-reported events for the cited work

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Observation b28129e7-5b0a-48e6-96ca-b552735a904a · outbound

This paper cites Wasser- stein distance guided representation learning for domain adap- tation.

ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization Wasser- stein distance guided representation learning for domain adap- tation

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:23:00.823806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ae0ecfdb-55f9-4870-8fad-f6930c04b1be · outbound

This paper cites Domain adaptation: challenges, methods, datasets, and applications.

ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization Domain adaptation: challenges, methods, datasets, and applications

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:23:00.812613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e82c2006-771c-4225-873b-0e03eddb9ee9 · outbound

This paper cites On the Origin of Implicit Regularization in Stochastic Gradient Descent.

ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization On the Origin of Implicit Regularization in Stochastic Gradient Descent

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-12T04:23:00.414541Z

Source-reported events for the cited work

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Observation 4e88e56a-21e4-461e-bc3a-07d4eff22f76 · outbound

This paper cites How does Early Stopping Help Generalization against Label Noise?.

ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization How does Early Stopping Help Generalization against Label Noise?

Reference 36

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Observation 5d4d2119-f3c5-456a-ae05-adf7a278d7dc · outbound

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

ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization Dropout: A simple way to prevent neural networks from overfitting

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:23:00.803561Z

Source-reported events for the cited work

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Observation b151c7b4-d318-4317-a2ec-77d711cf3825 · outbound

This paper cites Rethinking the inception ar- chitecture for computer vision.

ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization Rethinking the inception ar- chitecture for computer vision

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:23:00.793751Z

Source-reported events for the cited work

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Observation c4e08df1-17d8-403b-ac49-0a43463b86d2 · outbound

This paper cites AuG-KD: Anchor-Based Mixup Generation for Out-of-Domain Knowledge Distillation.

ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization AuG-KD: Anchor-Based Mixup Generation for Out-of-Domain Knowledge Distillation

Reference 39

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Source-reported events for the cited work

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Observation 24f71351-e395-4e57-bdc3-d8432f4b1a72 · outbound

This paper cites A comprehensive survey on regularization strategies in machine learning.

ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization A comprehensive survey on regularization strategies in machine learning

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:23:00.779952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 60fda121-4bce-4088-a08e-3206d798a9fa · outbound

This paper cites Efficient object localization using con- volutional networks.

ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization Efficient object localization using con- volutional networks

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:23:00.766244Z

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Observation e08278c6-934e-47c2-861a-3115b1205b91 · outbound

This paper cites Deep Domain Confusion: Maximizing for Domain Invariance.

ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization Deep Domain Confusion: Maximizing for Domain Invariance

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-12T04:23:00.446450Z

Source-reported events for the cited work

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Observation c4702bd8-9440-4310-b68a-77a38739f325 · outbound

This paper cites Adversarial discriminative domain adaptation.

ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization Adversarial discriminative domain adaptation

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-12T04:23:00.451292Z

Source-reported events for the cited work

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Observation 3ea03c33-d6a4-4078-a5f0-cf65983dcf3e · outbound

This paper cites On the uniform convergence of relative frequencies of events to their prob- abilities.

ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization On the uniform convergence of relative frequencies of events to their prob- abilities

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:23:00.745759Z

Source-reported events for the cited work

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Observation e5ba0ba5-8739-415b-b7d4-d1965eacfd3c · outbound

This paper cites Bayesian learning via stochas- tic gradient langevin dynamics.

ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization Bayesian learning via stochas- tic gradient langevin dynamics

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-12T04:23:00.459089Z

Source-reported events for the cited work

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Observation e61b96ea-70ed-4b3d-a72d-b808d38a94e5 · outbound

This paper cites Group normalization.

ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization Group normalization

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:23:00.725770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6c8e1797-dd61-45e4-9e25-ba422eb6ab96 · outbound

This paper cites Adversarial Domain Adaptation for Stance Detection.

ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization Adversarial Domain Adaptation for Stance Detection

Reference 47

Resolution: verified exact; local_arxiv, observed 2026-08-12T04:23:00.534572Z

Source-reported events for the cited work

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Observation 45ee6134-71bf-4440-8753-d2d730ebbada · outbound

This paper cites Understanding deep learning (still) re- quires rethinking generalization.

ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization Understanding deep learning (still) re- quires rethinking generalization

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:23:00.714349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c52ddac5-a0f2-4074-b3da-e1c70aa11030 · outbound

This paper cites ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization.

ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization

Reference 49

Resolution: metadata mismatch; local_arxiv, observed 2026-08-12T04:23:00.515259Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

Observation c52ddac5-a0f2-4074-b3da-e1c70aa11030 · inbound