OMENN: One Matrix to Explain Neural Networks | Paper Citation Record

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This paper cites Survey on explainable ai: techniques, challenges and open issues.

OMENN: One Matrix to Explain Neural Networks Survey on explainable ai: techniques, challenges and open issues

Reference 1

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Observation 9079b25e-df69-4e48-b102-59444d20f45f · outbound

This paper cites Sanity checks for saliency maps.

OMENN: One Matrix to Explain Neural Networks Sanity checks for saliency maps

Reference 2

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Observation 01a2243d-dba0-45da-b028-87d0bdba96a0 · outbound

This paper cites Towards robust interpretability with self-explaining neural networks.

OMENN: One Matrix to Explain Neural Networks Towards robust interpretability with self-explaining neural networks

Reference 3

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Observation 403b5794-dc19-44df-975a-d7db4a7c481d · outbound

This paper cites Diffusion visual counterfactual explana- tions.

OMENN: One Matrix to Explain Neural Networks Diffusion visual counterfactual explana- tions

Reference 4

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Observation bef916fb-6472-4fad-926b-9ec5b3c14555 · outbound

This paper cites On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation.

OMENN: One Matrix to Explain Neural Networks On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation

Reference 5

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Observation 452ada35-0ebf-46da-9129-fb29de6cee2b · outbound

This paper cites an unresolved cited work.

OMENN: One Matrix to Explain Neural Networks Unresolved cited work

Reference 6

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Observation 6e9c0972-2695-494f-9171-b40f491482f4 · outbound

This paper cites Shortcomings of top-down randomization-based sanity checks for evaluations of deep neural network ex- planations.

OMENN: One Matrix to Explain Neural Networks Shortcomings of top-down randomization-based sanity checks for evaluations of deep neural network ex- planations

Reference 7

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Observation 0222c0a8-aadf-4120-b14c-ef40c04132e0 · outbound

This paper cites B-cos net- works: Alignment is all we need for interpretability.

OMENN: One Matrix to Explain Neural Networks B-cos net- works: Alignment is all we need for interpretability

Reference 8

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Observation c751d14a-2d7b-471a-9abb-7ee46309a0d8 · outbound

This paper cites B-cos alignment for inherently interpretable cnns and vision transformers.

OMENN: One Matrix to Explain Neural Networks B-cos alignment for inherently interpretable cnns and vision transformers

Reference 9

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Observation d589d069-fcc7-44a4-a5ac-d43b42660835 · outbound

This paper cites Grad-cam++: General- ized gradient-based visual explanations for deep convolu- tional networks.

OMENN: One Matrix to Explain Neural Networks Grad-cam++: General- ized gradient-based visual explanations for deep convolu- tional networks

Reference 10

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Observation 3090ccc9-76e2-4f97-b175-00c798eee5e2 · outbound

This paper cites Transformer inter- pretability beyond attention visualization.

OMENN: One Matrix to Explain Neural Networks Transformer inter- pretability beyond attention visualization

Reference 11

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Observation 1b940a15-7221-48d7-aee3-7d03c02ef3d1 · outbound

This paper cites This looks like that: deep learn- ing for interpretable image recognition.

OMENN: One Matrix to Explain Neural Networks This looks like that: deep learn- ing for interpretable image recognition

Reference 12

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Observation a08cba16-7ef0-4a84-b4e1-f242c688b19a · outbound

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

OMENN: One Matrix to Explain Neural Networks Imagenet: A large-scale hierarchical image database

Reference 13

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Observation c8fa31e4-b9ea-44db-8d36-16b014d2f15f · outbound

This paper cites Learning without mem- orizing.

OMENN: One Matrix to Explain Neural Networks Learning without mem- orizing

Reference 14

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Observation 39280a5f-ca11-40df-911e-ab7c7e650ce4 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale, 2021.

OMENN: One Matrix to Explain Neural Networks An image is worth 16x16 words: Transformers for image recognition at scale, 2021

Reference 15

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Observation e8797a71-70ef-4001-8add-c7d9cfdaac16 · outbound

This paper cites Don’t lie to me! robust and efficient explainabil- ity with verified perturbation analysis.

OMENN: One Matrix to Explain Neural Networks Don’t lie to me! robust and efficient explainabil- ity with verified perturbation analysis

Reference 16

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Observation 31ef13b3-6c50-431b-a7be-e0c5d0ebf50a · outbound

This paper cites Interpretable explana- tions of black boxes by meaningful perturbation.

OMENN: One Matrix to Explain Neural Networks Interpretable explana- tions of black boxes by meaningful perturbation

Reference 17

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Observation ffdf4624-d5cf-4e4f-8fb4-44df25bfac53 · outbound

This paper cites This looks more like that: Enhancing self-explaining models by prototypical rele- vance propagation.

OMENN: One Matrix to Explain Neural Networks This looks more like that: Enhancing self-explaining models by prototypical rele- vance propagation

Reference 18

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Observation 9c91efed-6690-46e8-ac48-201459a571b4 · outbound

This paper cites Towards automatic concept-based explanations.

OMENN: One Matrix to Explain Neural Networks Towards automatic concept-based explanations

Reference 19

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Observation 91a81f9a-71df-4168-87b6-ede0b030039c · outbound

This paper cites Counterfactual visual explanations.

OMENN: One Matrix to Explain Neural Networks Counterfactual visual explanations

Reference 20

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Observation 486415cb-2e8f-437c-872f-4f7a194b6fa6 · outbound

This paper cites Deep residual learning for image recognition.

OMENN: One Matrix to Explain Neural Networks Deep residual learning for image recognition

Reference 21

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Observation 12053626-042f-4303-acc1-0f704311dc71 · outbound

This paper cites Quantus: An explain- able ai toolkit for responsible evaluation of neural network explanations and beyond.

OMENN: One Matrix to Explain Neural Networks Quantus: An explain- able ai toolkit for responsible evaluation of neural network explanations and beyond

Reference 22

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Observation 1d0b94fa-f620-45cf-9440-7c698a514ec7 · outbound

This paper cites A fresh look at sanity checks for saliency maps.

OMENN: One Matrix to Explain Neural Networks A fresh look at sanity checks for saliency maps

Reference 23

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Observation 7c0896db-877d-4383-a0b1-ee5758da4ad3 · outbound

This paper cites Fast and flexible convolutional sparse coding.

OMENN: One Matrix to Explain Neural Networks Fast and flexible convolutional sparse coding

Reference 24

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Observation 2efec1e4-56c4-43d2-a97f-f5900c456275 · outbound

This paper cites Gaussian Error Linear Units (GELUs).

OMENN: One Matrix to Explain Neural Networks Gaussian Error Linear Units (GELUs)

Reference 25

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Observation 67f8db3d-3094-4685-a961-3f4b434e299b · outbound

This paper cites Fun- nybirds: A synthetic vision dataset for a part-based analysis of explainable ai methods.

OMENN: One Matrix to Explain Neural Networks Fun- nybirds: A synthetic vision dataset for a part-based analysis of explainable ai methods

Reference 26

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Observation 7f6ea3af-28d2-49eb-8cc7-0792a67649ab · outbound

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

OMENN: One Matrix to Explain Neural Networks Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Reference 27

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Observation ab06dbd0-9e7a-488c-a21a-1b224e67adb3 · outbound

This paper cites Steex: steering counter- factual explanations with semantics.

OMENN: One Matrix to Explain Neural Networks Steex: steering counter- factual explanations with semantics

Reference 28

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Observation 089d2cae-2db4-4a49-8be8-5df75a65e603 · outbound

This paper cites Ad- versarial counterfactual visual explanations.

OMENN: One Matrix to Explain Neural Networks Ad- versarial counterfactual visual explanations

Reference 29

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Observation 23e9926f-b3a8-4ad8-8704-ad9af0dacd4c · outbound

This paper cites Interpretability be- yond feature attribution: Quantitative testing with concept activation vectors (tcav).

OMENN: One Matrix to Explain Neural Networks Interpretability be- yond feature attribution: Quantitative testing with concept activation vectors (tcav)

Reference 30

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Observation 5a5a5515-a2a8-4ca1-9594-2cd7ad9e6066 · outbound

This paper cites Hive: Evaluating the human interpretability of visual explanations.

OMENN: One Matrix to Explain Neural Networks Hive: Evaluating the human interpretability of visual explanations

Reference 31

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Observation 379fed0b-ab16-4f69-b831-e2428f341ed5 · outbound

This paper cites Concept bottleneck models.

OMENN: One Matrix to Explain Neural Networks Concept bottleneck models

Reference 32

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This paper cites Visual concept connectome (vcc): Open world con- cept discovery and their interlayer connections in deep mod- els.

OMENN: One Matrix to Explain Neural Networks Visual concept connectome (vcc): Open world con- cept discovery and their interlayer connections in deep mod- els

Reference 33

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This paper cites Imagenet classification with deep convolutional neural net- works.

OMENN: One Matrix to Explain Neural Networks Imagenet classification with deep convolutional neural net- works

Reference 34

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Observation 3ec6eb84-de22-4fbc-80f2-c3ac879e3295 · outbound

This paper cites Layer normalization.

OMENN: One Matrix to Explain Neural Networks Layer normalization

Reference 35

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This paper cites Towards visually explaining video under- standing networks with perturbation.

OMENN: One Matrix to Explain Neural Networks Towards visually explaining video under- standing networks with perturbation

Reference 36

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Observation 40d578fb-d1e7-44ff-829b-376983484e23 · outbound

This paper cites A convnet for the 2020s.

OMENN: One Matrix to Explain Neural Networks A convnet for the 2020s

Reference 37

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This paper cites Rec- tifier nonlinearities improve neural network acoustic models.

OMENN: One Matrix to Explain Neural Networks Rec- tifier nonlinearities improve neural network acoustic models

Reference 38

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Observation 0488cc66-e2b8-4ae0-a43c-34d16df16a3b · outbound

This paper cites Layer-wise relevance propagation: an overview.Explainable AI: interpreting, explaining and visualizing deep learning , pages 193–209, 2019.

OMENN: One Matrix to Explain Neural Networks Layer-wise relevance propagation: an overview.Explainable AI: interpreting, explaining and visualizing deep learning , pages 193–209, 2019

Reference 39

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Observation 7aad064e-f4d7-4448-8fc8-d35be9d3e94b · outbound

This paper cites Pip-net: Patch-based intuitive prototypes for interpretable image classification.

OMENN: One Matrix to Explain Neural Networks Pip-net: Patch-based intuitive prototypes for interpretable image classification

Reference 40

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Observation f0d0a487-38f1-4beb-9470-47550c177868 · outbound

This paper cites Q-senn: Quantized self-explaining neural networks.

OMENN: One Matrix to Explain Neural Networks Q-senn: Quantized self-explaining neural networks

Reference 41

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This paper cites A sur- vey of the usages of deep learning for natural language pro- cessing.

OMENN: One Matrix to Explain Neural Networks A sur- vey of the usages of deep learning for natural language pro- cessing

Reference 42

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This paper cites LucidPPN: Unambiguous Prototypical Parts Network for User-centric Interpretable Computer Vision.

OMENN: One Matrix to Explain Neural Networks LucidPPN: Unambiguous Prototypical Parts Network for User-centric Interpretable Computer Vision

Reference 43

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This paper cites Good Teachers Explain: Explanation-Enhanced Knowledge Distillation.

OMENN: One Matrix to Explain Neural Networks Good Teachers Explain: Explanation-Enhanced Knowledge Distillation

Reference 44

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OMENN: One Matrix to Explain Neural Networks Unresolved cited work

Reference 45

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This paper cites ” why should i trust you?” explaining the predictions of any classifier.

OMENN: One Matrix to Explain Neural Networks ” why should i trust you?” explaining the predictions of any classifier

Reference 46

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This paper cites Stop explaining black box machine learn- ing models for high stakes decisions and use interpretable models instead.

OMENN: One Matrix to Explain Neural Networks Stop explaining black box machine learn- ing models for high stakes decisions and use interpretable models instead

Reference 47

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This paper cites Protopshare: Prototypical parts sharing for simi- larity discovery in interpretable image classification.

OMENN: One Matrix to Explain Neural Networks Protopshare: Prototypical parts sharing for simi- larity discovery in interpretable image classification

Reference 48

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Observation 6d442370-d987-4dce-b5a2-7f596a733fc2 · outbound

This paper cites In- terpretable image classification with differentiable proto- types assignment.

OMENN: One Matrix to Explain Neural Networks In- terpretable image classification with differentiable proto- types assignment

Reference 49

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This paper cites Towards explain- able artificial intelligence.

OMENN: One Matrix to Explain Neural Networks Towards explain- able artificial intelligence

Reference 50

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This paper cites Grad-cam: Visual explanations from deep networks via gradient-based localization.

OMENN: One Matrix to Explain Neural Networks Grad-cam: Visual explanations from deep networks via gradient-based localization

Reference 51

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This paper cites Very deep convo- lutional networks for large-scale image recognition, 2015.

OMENN: One Matrix to Explain Neural Networks Very deep convo- lutional networks for large-scale image recognition, 2015

Reference 52

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This paper cites Full-gradient represen- tation for neural network visualization.

OMENN: One Matrix to Explain Neural Networks Full-gradient represen- tation for neural network visualization

Reference 53

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This paper cites Axiomatic attribution for deep networks.

OMENN: One Matrix to Explain Neural Networks Axiomatic attribution for deep networks

Reference 54

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This paper cites Post-hoc Part-prototype Networks.

OMENN: One Matrix to Explain Neural Networks Post-hoc Part-prototype Networks

Reference 55

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Observation e72b931e-9fe4-45a9-a2e7-7719fd1c2a10 · outbound

This paper cites Sanity checks for saliency metrics.

OMENN: One Matrix to Explain Neural Networks Sanity checks for saliency metrics

Reference 56

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Observation 45400f22-9e3a-4500-8a93-aed26e2e30f0 · outbound

This paper cites Deep learning for 10 computer vision: A brief review.

OMENN: One Matrix to Explain Neural Networks Deep learning for 10 computer vision: A brief review

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:39:21.883332Z

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Observation 8fa26e43-b253-45fd-ae91-3737e2f7b279 · outbound

This paper cites Effi- cient mobile implementation of a cnn-based object recogni- tion system.

OMENN: One Matrix to Explain Neural Networks Effi- cient mobile implementation of a cnn-based object recogni- tion system

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:39:21.874893Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c3a5d92b-32a7-47cc-8fbd-15bb4773ff83 · outbound

This paper cites Self-attention generative adversarial networks.

OMENN: One Matrix to Explain Neural Networks Self-attention generative adversarial networks

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-11T23:39:21.793820Z

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Unavailable: canonical work link unavailable.

Observation c566b537-a2c2-4e43-8a4a-63c3988ebeee · outbound

This paper cites Shap- cam: Visual explanations for convolutional neural networks based on shapley value.

OMENN: One Matrix to Explain Neural Networks Shap- cam: Visual explanations for convolutional neural networks based on shapley value

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:39:21.861455Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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