Relevance-driven Input Dropout: an Explanation-guided Regularization Technique | Paper Citation Record

Observation b1cd50f1-99f0-4258-ad00-7a1b17bb6acd · outbound

This paper cites From attribution maps to human-understandable explanations through concept relevance propagation.Nature Machine Intelligence, 5(9):1006–1019, 2023.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique From attribution maps to human-understandable explanations through concept relevance propagation.Nature Machine Intelligence, 5(9):1006–1019, 2023

Reference 1

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This paper cites Attnlrp: Attention-aware layer-wise relevance propagation for transformers.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Attnlrp: Attention-aware layer-wise relevance propagation for transformers

Reference 2

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This paper cites Software for Dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAy.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Software for Dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAy

Reference 3

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This paper cites CLEVR-XAI: A benchmark dataset for the ground truth evaluation of neural network explanations.Inf.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique CLEVR-XAI: A benchmark dataset for the ground truth evaluation of neural network explanations.Inf

Reference 4

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Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Unresolved cited work

Reference 5

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Observation 5b40b367-4a12-4e71-b4eb-2eabaedba2ce · outbound

This paper cites On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation.PLoS ONE, 10(7):e0130140, 2015.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation.PLoS ONE, 10(7):e0130140, 2015

Reference 6

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This paper cites How to explain individual classification decisions.Journal of Machine Learning Research, 11: 1803–1831, 2010.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique How to explain individual classification decisions.Journal of Machine Learning Research, 11: 1803–1831, 2010

Reference 7

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This paper cites Network dissection: Quanti- fying interpretability of deep visual representations.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Network dissection: Quanti- fying interpretability of deep visual representations

Reference 8

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This paper cites Ecq x: Explainability-driven quantization for low-bit and sparse dnns.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Ecq x: Explainability-driven quantization for low-bit and sparse dnns

Reference 9

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This paper cites Calmon, and Himabindu Lakkaraju.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Calmon, and Himabindu Lakkaraju

Reference 10

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This paper cites GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks

Reference 11

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This paper cites Artificial intelligence in medicine: today and tomorrow.Frontiers in Medicine, 7:509744, 2020.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Artificial intelligence in medicine: today and tomorrow.Frontiers in Medicine, 7:509744, 2020

Reference 12

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Observation 2067eb7e-850b-4f6a-8d2f-f7b66feab9e7 · outbound

This paper cites Roberts, and Chris C.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Roberts, and Chris C

Reference 13

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This paper cites ShapeNet: An Information-Rich 3D Model Repository.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique ShapeNet: An Information-Rich 3D Model Repository

Reference 14

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This paper cites Cubuk, Barret Zoph, Dandelion Mané, Vijay Vasudevan, and Quoc V.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Cubuk, Barret Zoph, Dandelion Mané, Vijay Vasudevan, and Quoc V

Reference 15

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

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Imagenet: A large-scale hierarchical image database

Reference 16

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This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique An image is worth 16x16 words: Transformers for image recognition at scale

Reference 17

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This paper cites Mechanistic understanding and validation of large AI models with SemanticLens.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Mechanistic understanding and validation of large AI models with SemanticLens

Reference 18

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Observation 59d4ac06-1273-4ad4-9a91-10d019e21209 · outbound

This paper cites Explain to not forget: Defending against catastrophic forgetting with XAI.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Explain to not forget: Defending against catastrophic forgetting with XAI

Reference 19

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This paper cites Fong and Andrea Vedaldi.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Fong and Andrea Vedaldi

Reference 20

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Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Unresolved cited work

Reference 21

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This paper cites The Missing Curve Detectors of InceptionV1: Applying Sparse Autoencoders to InceptionV1 Early Vision.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique The Missing Curve Detectors of InceptionV1: Applying Sparse Autoencoders to InceptionV1 Early Vision

Reference 22

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This paper cites Martin, and Shi-Min Hu.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Martin, and Shi-Min Hu

Reference 23

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This paper cites Pruning By Explaining Revisited: Optimizing Attribution Methods to Prune CNNs and Transformers.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Pruning By Explaining Revisited: Optimizing Attribution Methods to Prune CNNs and Transformers

Reference 24

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This paper cites Deep residual learning for image recognition.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Deep residual learning for image recognition

Reference 25

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Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Unresolved cited work

Reference 26

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This paper cites The many faces of robustness: A critical analysis of out-of-distribution generalization.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique The many faces of robustness: A critical analysis of out-of-distribution generalization

Reference 27

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This paper cites Natural adversarial examples.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Natural adversarial examples

Reference 28

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

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Improving neural networks by preventing co-adaptation of feature detectors

Reference 29

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This paper cites Summit: Scaling deep learning interpretability by visualizing activation and attribution summarizations.IEEE Trans.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Summit: Scaling deep learning interpretability by visualizing activation and attribution summarizations.IEEE Trans

Reference 30

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This paper cites Architecture disentanglement for deep neural networks.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Architecture disentanglement for deep neural networks

Reference 31

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This paper cites Sparse autoencoders find highly interpretable features in language models.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Sparse autoencoders find highly interpretable features in language models

Reference 32

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This paper cites Batch normalization: Accelerating deep network training by reducing internal covariate shift.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Batch normalization: Accelerating deep network training by reducing internal covariate shift

Reference 33

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This paper cites PatchShuffle Regularization.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique PatchShuffle Regularization

Reference 34

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This paper cites Cai, James Wexler, Fernanda B.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Cai, James Wexler, Fernanda B

Reference 35

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This paper cites Dropout as data augmentation.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Dropout as data augmentation

Reference 36

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This paper cites Derpanis, and Pavel Tokmakov.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Derpanis, and Pavel Tokmakov

Reference 37

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This paper cites Krizhevsky and G.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Krizhevsky and G

Reference 38

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Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Unresolved cited work

Reference 39

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Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Unresolved cited work

Reference 40

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This paper cites Improvement in deep networks for optimization using explainable artificial intelligence.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Improvement in deep networks for optimization using explainable artificial intelligence

Reference 41

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This paper cites R-drop: Regularized dropout for neural networks.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique R-drop: Regularized dropout for neural networks

Reference 42

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This paper cites Decoupled weight decay regularization.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Decoupled weight decay regularization

Reference 43

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This paper cites Lundberg and Su-In Lee.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Lundberg and Su-In Lee

Reference 44

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This paper cites Explaining nonlinear classification decisions with deep taylor decomposition.Pattern Recognition, 65: 211–222, 2017.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Explaining nonlinear classification decisions with deep taylor decomposition.Pattern Recognition, 65: 211–222, 2017

Reference 45

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This paper cites Layer-wise relevance propagation: An overview.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Layer-wise relevance propagation: An overview

Reference 46

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This paper cites Measurably stronger explanation reliability via model canonization.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Measurably stronger explanation reliability via model canonization

Reference 47

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Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Unresolved cited work

Reference 48

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This paper cites xAI-GAN: Enhancing Generative Adversarial Networks via Explainable AI Systems.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique xAI-GAN: Enhancing Generative Adversarial Networks via Explainable AI Systems

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This paper cites Regularizing deep neural networks by noise: Its interpretation and optimization.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Regularizing deep neural networks by noise: Its interpretation and optimization

Reference 50

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Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Unresolved cited work

Reference 51

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Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Unresolved cited work

Reference 52

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Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Unresolved cited work

Reference 53

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Observation ec242fd1-9958-4b56-b66d-808f52a83d16 · outbound

This paper cites why should I trust you?.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique why should I trust you?

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:15.348969Z

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 fc34e885-7fbb-423b-aa56-d9ad1a7974de · outbound

This paper cites Utilizing Explainable AI for Quantization and Pruning of Deep Neural Networks.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Utilizing Explainable AI for Quantization and Pruning of Deep Neural Networks

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-07T13:35:55.554744Z

Source-reported events for the cited work

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Observation 34168191-7da3-4e7b-b5f8-fd28fc97b2bc · outbound

This paper cites Evaluating the visualization of what a deep neural network has learned.IEEE Trans.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Evaluating the visualization of what a deep neural network has learned.IEEE Trans

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:15.193012Z

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 9bf5d223-fd3f-4cba-9c3b-ff0e90c8d2de · outbound

This paper cites Learning important features through propa- gating activation differences.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Learning important features through propa- gating activation differences

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:15.059313Z

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 eb8f92d5-af72-4913-9ba2-9606a981ffcf · outbound

This paper cites Hide-and-seek: Forcing a network to be meticulous for weakly- supervised object and action localization.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Hide-and-seek: Forcing a network to be meticulous for weakly- supervised object and action localization

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:14.938347Z

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 6c331e1b-1093-4f53-acef-c9144f9b7fe9 · outbound

This paper cites Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:14.725884Z

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 27d2ed9d-83e6-4903-954b-fabbe56205df · outbound

This paper cites Explaining prediction models and individual predictions with feature contributions.Knowledge and Information Systems, 41(3):647–665, 2014.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Explaining prediction models and individual predictions with feature contributions.Knowledge and Information Systems, 41(3):647–665, 2014

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:36:03.795559Z

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 39ef27a1-a71a-45cf-ab33-89f2e9dbe32a · outbound

This paper cites Axiomatic attribution for deep networks.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Axiomatic attribution for deep networks

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:36:03.624235Z

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 07a40c24-e6aa-46b3-9d3f-c6bff6024538 · outbound

This paper cites Multi-dimensional concept discovery (MCD): A unifying framework with completeness guarantees.Trans.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Multi-dimensional concept discovery (MCD): A unifying framework with completeness guarantees.Trans

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:36:03.401392Z

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 eeac9ef3-fd86-400e-b84b-0f19d7f9d0b3 · outbound

This paper cites Analyzing multi-head self- attention: Specialized heads do the heavy lifting, the rest can be pruned.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Analyzing multi-head self- attention: Specialized heads do the heavy lifting, the rest can be pruned

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:36:03.198130Z

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 2173cb85-5c3e-401d-81b5-22b8b6ae582f · outbound

This paper cites Zeiler, Sixin Zhang, Yann LeCun, and Rob Fergus.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Zeiler, Sixin Zhang, Yann LeCun, and Rob Fergus

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:36:02.957180Z

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 7159339b-9ce5-4b96-abdd-d7362bef8a64 · outbound

This paper cites Sarma, Michael M.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Sarma, Michael M

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:36:02.821877Z

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 b52e0257-79b2-4765-9598-ee5cf0aeb1cc · outbound

This paper cites Efficient and Flexible Neural Network Training through Layer-wise Feedback Propagation.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Efficient and Flexible Neural Network Training through Layer-wise Feedback Propagation

Reference 66

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:35:58.732346Z

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 07f68074-75b4-4887-a18e-2d155b9fafd2 · outbound

This paper cites Wei and Kai Zou.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Wei and Kai Zou

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:36:02.479725Z

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 9eb6c8df-8f4a-4878-bc1a-8e00e2745c72 · outbound

This paper cites Pytorch image models.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Pytorch image models

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-07T13:35:56.776805Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e1f91f76-27f7-4e96-ab58-e79f0d69fdb7 · outbound

This paper cites 3d shapenets: A deep representation for volumetric shapes.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique 3d shapenets: A deep representation for volumetric shapes

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:36:02.288059Z

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 0df50dce-10de-4b03-af9c-1b9a64189e64 · outbound

This paper cites Disturblabel: Regularizing CNN on the loss layer.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Disturblabel: Regularizing CNN on the loss layer

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:36:02.080950Z

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 44e48d6c-9e97-4790-b6b6-7637da0c34c1 · outbound

This paper cites Pointnet/pointnet2pytorch.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Pointnet/pointnet2pytorch

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:36:01.751921Z

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 b9e5f3fc-518a-445d-8ee7-12ab26155273 · outbound

This paper cites AD-DROP: attribution-driven dropout for robust language model fine-tuning.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique AD-DROP: attribution-driven dropout for robust language model fine-tuning

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:36:01.466675Z

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 9e5d9e9f-0dda-421a-8c89-ba613e6fdb70 · outbound

This paper cites Pruning by explaining: A novel criterion for deep neural network pruning.Pattern Recognition, 115:107899, 2021.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Pruning by explaining: A novel criterion for deep neural network pruning.Pattern Recognition, 115:107899, 2021

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:36:01.236413Z

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 994969bf-9394-49fc-8953-9f917ff88e35 · outbound

This paper cites Cutmix: Regularization strategy to train strong classifiers with localizable features.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Cutmix: Regularization strategy to train strong classifiers with localizable features

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:36:01.055954Z

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 b1cdd71e-8911-4ebd-bf69-366fe7d32a78 · outbound

This paper cites Noise Injection-based Regularization for Point Cloud Processing.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Noise Injection-based Regularization for Point Cloud Processing

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-07T13:35:57.653440Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d22b5233-f1d7-4b91-8218-f8446147ffb0 · outbound

This paper cites Zeiler and Rob Fergus.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Zeiler and Rob Fergus

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:36:00.744105Z

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 0b2196e4-06ee-45fd-bf25-6c672124e333 · outbound

This paper cites Dauphin, and David Lopez-Paz.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Dauphin, and David Lopez-Paz

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:36:00.517659Z

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 3955cd2d-d6e0-42ea-9aec-2dba16130010 · outbound

This paper cites Equivalence between dropout and data augmentation: A mathematical check.Neural Networks, 115:82–89, 2019.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Equivalence between dropout and data augmentation: A mathematical check.Neural Networks, 115:82–89, 2019

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:36:00.332444Z

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 30a1b9f2-646e-4ed7-9da8-42ecf43da4a2 · outbound

This paper cites Random erasing data augmentation.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Random erasing data augmentation

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:36:00.099443Z

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 cbed970e-880f-464c-9d0c-ab40924ccc52 · outbound

This paper cites Zintgraf, Taco S.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Zintgraf, Taco S

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:35:59.918900Z

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 b0a29aae-e437-468a-9629-2fc1fda8932d · outbound

This paper cites Regularization and variable selection via the elastic net.Journal of the Royal Statistical Society Series B: Statistical Methodology, 67(2):301–320, 03 2005.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique Regularization and variable selection via the elastic net.Journal of the Royal Statistical Society Series B: Statistical Methodology, 67(2):301–320, 03 2005

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:35:59.643013Z

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 045d50ed-142f-4765-ad0e-e609bbb7d6c7 · outbound

This paper cites RE data augmentation.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique RE data augmentation

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:35:59.359018Z

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 802d32f6-50a8-46d2-a137-dd16f4528df0 · outbound

This paper cites 3084– 3092, 2013.

Relevance-driven Input Dropout: an Explanation-guided Regularization Technique 3084– 3092, 2013

Reference 2013

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:22.831611Z

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.

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