Paper Citation Record · LEDGER

H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers

As of 6 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 1 inbound Pith citation observation for arXiv:2604.22045.

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

pith.paper-citation-record.v1
2604.22045 v1

Coverage vector

measured 51 of 51 reference resolution

Typed states for the displayed outbound observations.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-26T18:53:52.065940Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-04T02:49:25.315934Z

Reference resolution

51 of 51 outbound references displayed

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External citation measurements

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Outbound references

Observation 70e49092-a862-464f-8ea7-332b7676df45 · outbound

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

H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation.PloS one, 10 (7):e0130140

Reference 1

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Observation 2d70576a-949a-470e-b449-a8b2d9c16633 · outbound

This paper cites Evaluating and Aggregating Feature-based Model Explanations.

H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers Evaluating and Aggregating Feature-based Model Explanations

Reference 2

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Observation c5325ddc-572e-487d-86e5-35b850aaaaaf · outbound

This paper cites Sok: Modeling explainabil- ity in security analytics for interpretability, trustworthiness, and usability.

H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers Sok: Modeling explainabil- ity in security analytics for interpretability, trustworthiness, and usability

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T15:53:10.366412Z

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Observation b9b003dd-dfe6-41e4-8a3e-d1f2197c7993 · outbound

This paper cites Face: Faithful automatic concept extraction.

H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers Face: Faithful automatic concept extraction

Reference 4

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Observation 46a72a6c-cd7a-477c-91cd-46ea6605eb42 · outbound

This paper cites Preddiff: Explanations and interactions from conditional ex- pectations.Artificial Intelligence, 312:103774.

H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers Preddiff: Explanations and interactions from conditional ex- pectations.Artificial Intelligence, 312:103774

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T15:53:10.377881Z

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Observation 7ae7cb65-cc15-4e5c-bb13-bb54accf1d60 · outbound

This paper cites Concise explanations of neural net- works using adversarial training.

H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers Concise explanations of neural net- works using adversarial training

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T15:53:10.273600Z

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Observation 7d95dc2a-0be6-4562-bb24-1cb449f80d35 · outbound

This paper cites On hars]anyi dividends and asymmetric val- ues.International Game Theory Review, 19(03):1750012.

H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers On hars]anyi dividends and asymmetric val- ues.International Game Theory Review, 19(03):1750012

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T15:53:10.326512Z

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Observation 181d008f-0b01-475b-abf4-4e0a7382c06a · outbound

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

H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers Imagenet: A large-scale hierarchical image database

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T15:53:10.277930Z

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Observation 025ec7c9-16d6-4e37-82da-34cca826c06e · outbound

This paper cites Improving performance of deep learning models with axiomatic attribution priors and ex- pected gradients.Nature machine intelligence, 3(7):620– 631.

H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers Improving performance of deep learning models with axiomatic attribution priors and ex- pected gradients.Nature machine intelligence, 3(7):620– 631

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T15:53:10.315058Z

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Observation d77fea19-433d-4b46-882f-9fca84fe920d · outbound

This paper cites Craft: Concept recursive activation factoriza- tion for explainability.

H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers Craft: Concept recursive activation factoriza- tion for explainability

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T15:53:10.318951Z

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Observation db293e85-37c7-4d67-8ff9-71603b2e3cd7 · outbound

This paper cites Un- derstanding deep networks via extremal perturbations and smooth masks.

H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers Un- derstanding deep networks via extremal perturbations and smooth masks

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T15:53:10.261068Z

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Observation aca7530f-cf7a-47d4-858b-af931936ee9e · outbound

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

H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers Interpretable explana- tions of black boxes by meaningful perturbation

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T15:53:10.341679Z

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Observation 1ae84f92-07ea-46f3-8466-4cf32b6e1784 · outbound

This paper cites Predictive learn- ing via rule ensembles.

H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers Predictive learn- ing via rule ensembles

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T15:53:10.333931Z

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Observation 9fb6cf10-436e-4f15-b3a9-e8984da2cb92 · outbound

This paper cites Towards automatic concept-based explanations.Ad- vances in neural information processing systems, 32.

H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers Towards automatic concept-based explanations.Ad- vances in neural information processing systems, 32

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T15:53:10.345614Z

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Observation 356d70b0-bd84-4d6a-9e03-9bbdcf3f1095 · outbound

This paper cites A simplified bar- gaining model for the n-person cooperative game.Papers in game theory, pages 44–70.

H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers A simplified bar- gaining model for the n-person cooperative game.Papers in game theory, pages 44–70

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T15:53:10.269635Z

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Observation 5cf6f15d-3ced-4a00-b9d4-f8e148368ac7 · outbound

This paper cites Deep residual learning for image recognition.

H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers Deep residual learning for image recognition

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T15:53:10.303247Z

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Observation 84ab9874-ac64-48ae-a2c4-482a7ee6e9fb · outbound

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H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers Unresolved cited work

Reference 17

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Observation 80b03a0d-b4fb-4aea-9590-5e5287752a1a · outbound

This paper cites A benchmark for interpretability methods in deep neural networks.Advances in Neural Information Process- ing Systems, 32.

H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers A benchmark for interpretability methods in deep neural networks.Advances in Neural Information Process- ing Systems, 32

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T15:53:10.295546Z

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Observation de13c1b5-c018-45c4-af72-2a9920154d7e · outbound

This paper cites MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-05-11T14:36:05.655329Z

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Observation 6539aca7-ce0f-4669-a1e7-87254b851e09 · outbound

This paper cites Densely connected convolutional net- works.

H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers Densely connected convolutional net- works

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T15:53:10.397073Z

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Observation 532d36f0-5fb4-4977-af2d-fd00766ac3e8 · outbound

This paper cites Explain- ing explanations: Axiomatic feature interactions for deep networks.Journal of Machine Learning Research, 22(104): 1–54.

H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers Explain- ing explanations: Axiomatic feature interactions for deep networks.Journal of Machine Learning Research, 22(104): 1–54

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T15:53:10.381922Z

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Observation 1ac54680-75b3-4316-b79b-cb51cb7868fd · outbound

This paper cites Learning to under- stand: Identifying interactions via the m ¨obius transform.

H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers Learning to under- stand: Identifying interactions via the m ¨obius transform

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T15:53:10.418348Z

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Observation 102298d1-1133-4cac-adad-e6b39fa0d90b · outbound

This paper cites Guided integrated gradients: An adaptive path method for remov- ing noise.

H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers Guided integrated gradients: An adaptive path method for remov- ing noise

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T15:53:10.393445Z

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Observation e8fc5c9a-56f4-4916-b3f5-3755fedc297f · outbound

This paper cites Segment any- thing.

H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers Segment any- thing

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T15:53:10.410551Z

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Observation f53d6f61-1665-46d5-a2a9-fb0839f2f320 · outbound

This paper cites Captum: A unified and generic model interpretability library for pytorch.

H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers Captum: A unified and generic model interpretability library for pytorch

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T15:53:10.358284Z

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Observation 6ccb2739-7e48-4316-afef-db3d160fd516 · outbound

This paper cites Segment anything in medical images.Nature Communications, 15(1):654.

H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers Segment anything in medical images.Nature Communications, 15(1):654

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T15:53:10.406443Z

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Observation c1e93fc5-5f0a-4862-a5a9-8cab11085367 · outbound

This paper cites RISE: Randomized Input Sampling for Explanation of Black-box Models.

H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers RISE: Randomized Input Sampling for Explanation of Black-box Models

Reference 27

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Observation 33df7453-d691-4e7e-aec3-01fdd78f3f21 · outbound

This paper cites RISE: random- ized input sampling for explanation of black-box models.

H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers RISE: random- ized input sampling for explanation of black-box models

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T15:53:10.282377Z

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Observation bf4e823d-d392-4df7-80f1-915a5d107a9e · outbound

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

H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers ” why should i trust you?” explaining the predictions of any classifier

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T15:53:10.287611Z

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Observation aa5229f1-ba9a-496b-992c-1f4023903130 · outbound

This paper cites A Consistent and Efficient Evaluation Strategy for Attribution Methods.

H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers A Consistent and Efficient Evaluation Strategy for Attribution Methods

Reference 30

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T14:36:05.563818Z

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Observation f8906d29-19c4-4f9b-b5e2-f279757b756b · outbound

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

H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers Grad-cam: Visual explanations from deep networks via gradient-based localization

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T15:53:10.401742Z

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Observation 85484df9-7ec8-4aec-8d93-6b2f04782f83 · outbound

This paper cites Learning important features through propagating activation differences.

H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers Learning important features through propagating activation differences

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T15:53:10.385951Z

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Observation d4490df7-3cdd-4fbf-8534-d2d5724d1b06 · outbound

This paper cites Integrated directional gradients: Feature interaction attribu- tion for neural nlp models.

H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers Integrated directional gradients: Feature interaction attribu- tion for neural nlp models

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T15:53:10.299540Z

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Observation 877c0227-f09d-43d8-9aeb-8d3632cb06ee · outbound

This paper cites Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps.

H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-11T14:36:05.596745Z

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Observation 7dffdf68-5a34-49ef-ad22-94b29410ab3e · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 35

Resolution: verified exact; local_arxiv, observed 2026-05-11T14:36:05.622554Z

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Observation 3c3ee16e-fced-4b9b-8210-9ee3107b67a4 · outbound

This paper cites Hierarchical interpretations for neural network predictions.

H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers Hierarchical interpretations for neural network predictions

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-05-11T14:36:05.574006Z

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Observation cd9a75bb-1149-4397-a8ed-4d5d50450d12 · outbound

This paper cites Caso: Context-aware second-order interpre- tations.https://github.com/singlasahil14/ CASO.

H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers Caso: Context-aware second-order interpre- tations.https://github.com/singlasahil14/ CASO

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T15:53:10.322724Z

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Observation c7ec54e0-6a5f-41ab-87db-5b5ea541e7ea · outbound

This paper cites Un- derstanding impacts of high-order loss approximations and features in deep learning interpretation.

H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers Un- derstanding impacts of high-order loss approximations and features in deep learning interpretation

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T15:53:10.311176Z

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Observation a43a1d35-9cc8-4213-a0c1-a4c6ef0e2c61 · outbound

This paper cites SmoothGrad: removing noise by adding noise.

H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers SmoothGrad: removing noise by adding noise

Reference 40

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T14:36:05.586989Z

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Observation a77d6676-a9a2-4b51-9950-d65daf4860fc · outbound

This paper cites Detecting statistical interactions with additive groves of trees.

H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers Detecting statistical interactions with additive groves of trees

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T15:53:10.370095Z

Source-reported events for the cited work

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Observation b0afad7e-77ca-499c-bf11-08e57e6d21c1 · outbound

This paper cites Identifying important group of pix- els using interactions.https : / / github.

H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers Identifying important group of pix- els using interactions.https : / / github

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T15:53:10.265286Z

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Observation aef4f79a-52f8-4d30-8c6f-3d84c1733958 · outbound

This paper cites Identifying important group of pixels using interactions.

H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers Identifying important group of pixels using interactions

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T15:53:10.389642Z

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Observation 8b9fc1ba-ad72-4cab-b50e-22cfb7fd75d9 · outbound

This paper cites Axiomatic attribution for deep networks.

H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers Axiomatic attribution for deep networks

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T15:53:10.330349Z

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Observation ba5f6ada-98bb-4ee5-b531-05a291707fc7 · outbound

This paper cites The shapley taylor interaction index.

H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers The shapley taylor interaction index

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T15:53:10.337934Z

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Observation 05bdfc3e-5ba6-4efa-8204-011fe4588a0d · outbound

This paper cites Faith-shap: The faithful shapley interaction index.Journal of Machine Learning Research, 24(94):1–42.

H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers Faith-shap: The faithful shapley interaction index.Journal of Machine Learning Research, 24(94):1–42

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T15:53:10.414366Z

Source-reported events for the cited work

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Observation 472bb33f-d747-4d3c-9f69-93cdcdc91c65 · outbound

This paper cites How does this interaction affect me? in- terpretable attribution for feature interactions.https:// github.com/mtsang/archipelag.

H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers How does this interaction affect me? in- terpretable attribution for feature interactions.https:// github.com/mtsang/archipelag

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T15:53:10.252387Z

Source-reported events for the cited work

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Observation 04cdbd41-873c-4648-b648-3ff36232cac0 · outbound

This paper cites How does this interaction affect me? interpretable attribution for fea- ture interactions.Advances in neural information processing systems, 33:6147–6159.

H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers How does this interaction affect me? interpretable attribution for fea- ture interactions.Advances in neural information processing systems, 33:6147–6159

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T15:53:10.349653Z

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Observation 48655979-605e-4825-858f-0c97b4a23989 · outbound

This paper cites The caltech-ucsd birds-200-2011 dataset.

H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers The caltech-ucsd birds-200-2011 dataset

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T15:53:10.256833Z

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Observation 1d0b7da9-5998-4e76-bada-c55fa9322c88 · outbound

This paper cites Explaining Object Detectors via Collective Contribution of Pixels.

H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers Explaining Object Detectors via Collective Contribution of Pixels

Reference 50

Resolution: verified exact; arxiv_id, observed 2026-05-20T00:02:47.564954Z

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Observation ba01be22-c343-480d-99ae-eae749f88545 · outbound

This paper cites Semantic image segmentation with deep convolutional neural networks and quick shift.Symmetry, 12 (3):427.

H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers Semantic image segmentation with deep convolutional neural networks and quick shift.Symmetry, 12 (3):427

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T15:53:10.353922Z

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Observation a4426ee1-5ea5-4ad3-9f61-5e5c717b291a · outbound

This paper cites integral of sums.

H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers integral of sums

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T15:53:10.307098Z

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Pith citing papers

Observation cd09a319-2a44-41c5-99ff-2ad0ed96345d · inbound

The Representational Limit of Scalar Interactions: An Interventional Decomposition cites this paper.

The Representational Limit of Scalar Interactions: An Interventional Decomposition H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers

Reference 33

Resolution: verified exact; local_arxiv, observed 2026-07-04T02:49:25.317402Z

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