Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection | Paper Citation Record

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This paper cites A new metric based on association rules to assess feature- attribution explainability techniques for time series forecasting.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection A new metric based on association rules to assess feature- attribution explainability techniques for time series forecasting

Reference 1

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This paper cites Towards human-centered ex- plainable ai: A survey of user studies for model explanations.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Towards human-centered ex- plainable ai: A survey of user studies for model explanations

Reference 2

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This paper cites Explainable deep learning methods in medical image classification: A survey.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Explainable deep learning methods in medical image classification: A survey

Reference 3

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This paper cites Explainable Artificial Intelligence (XAI): Concepts, taxonomies, op- portunities and challenges toward responsible ai.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Explainable Artificial Intelligence (XAI): Concepts, taxonomies, op- portunities and challenges toward responsible ai

Reference 4

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This paper cites Sim2Word: Explaining similarity with representative attribute words via counter- factual explanations.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Sim2Word: Explaining similarity with representative attribute words via counter- factual explanations

Reference 5

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This paper cites Going beyond XAI: A systematic survey for explanation-guided learning.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Going beyond XAI: A systematic survey for explanation-guided learning

Reference 6

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This paper cites End-to-end autonomous driving: Challenges and frontiers.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection End-to-end autonomous driving: Challenges and frontiers

Reference 7

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This paper cites Less is more: Fewer interpretable region via submodular subset selection.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Less is more: Fewer interpretable region via submodular subset selection

Reference 8

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This paper cites Making sense of dependence: Ef- ficient black-box explanations using dependence measure.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Making sense of dependence: Ef- ficient black-box explanations using dependence measure

Reference 9

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This paper cites Unifying fourteen post-hoc attribution methods with tay- lor interactions.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Unifying fourteen post-hoc attribution methods with tay- lor interactions

Reference 10

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This paper cites Interpreting Object-level Foundation Models via Visual Precision Search.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Interpreting Object-level Foundation Models via Visual Precision Search

Reference 11

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This paper cites Illuminating salient contributions in neuron activation with attribution equilibrium.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Illuminating salient contributions in neuron activation with attribution equilibrium

Reference 12

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This paper cites Local interpretations for explainable natural language processing: A survey.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Local interpretations for explainable natural language processing: A survey

Reference 13

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This paper cites A review and benchmark of feature importance methods for neural networks.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection A review and benchmark of feature importance methods for neural networks

Reference 14

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This paper cites Explainable ai (xai): Core ideas, techniques, and solutions.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Explainable ai (xai): Core ideas, techniques, and solutions

Reference 15

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

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation

Reference 16

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This paper cites Axiomatic attribution for deep networks.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Axiomatic attribution for deep networks

Reference 17

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

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Grad-CAM: Visual explanations from deep networks via gradient-based localization

Reference 18

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This paper cites iGOS++: integrated gradient optimized saliency by bilateral perturbations.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection iGOS++: integrated gradient optimized saliency by bilateral perturbations

Reference 19

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This paper cites Gradient- based visual explanation for transformer-based clip.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Gradient- based visual explanation for transformer-based clip

Reference 20

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This paper cites A unified approach to interpreting model predictions.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection A unified approach to interpreting model predictions

Reference 21

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This paper cites Explain any concept: Segment anything meets concept-based explanation.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Explain any concept: Segment anything meets concept-based explanation

Reference 22

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

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection RISE: Randomized input sampling for explanation of black-box models

Reference 23

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Observation f2c9130b-d1f8-4b59-8645-d911021fb5b0 · outbound

This paper cites Defining and extracting generalizable interaction primitives from dnns.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Defining and extracting generalizable interaction primitives from dnns

Reference 24

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This paper cites Towards the difficulty for a deep neural network to learn concepts of different complexities.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Towards the difficulty for a deep neural network to learn concepts of different complexities

Reference 25

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This paper cites Fujishige, Submodular functions and optimization.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Fujishige, Submodular functions and optimization

Reference 26

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This paper cites Learning transferable visual models from natural language supervision.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Learning transferable visual models from natural language supervision

Reference 27

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This paper cites ImageBind: One embedding space to bind them all.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection ImageBind: One embedding space to bind them all

Reference 28

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This paper cites LanguageBind: Extending video-language pre- training to n-modality by language-based semantic alignment.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection LanguageBind: Extending video-language pre- training to n-modality by language-based semantic alignment

Reference 29

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Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Unresolved cited work

Reference 30

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Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Quilt-1M: One million image- text pairs for histopathology

Reference 31

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

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection ImageNet: A large-scale hierarchical image database

Reference 32

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This paper cites VGGSound: A large- scale audio-visual dataset.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection VGGSound: A large- scale audio-visual dataset

Reference 33

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This paper cites Caltech-UCSD Birds 200. Technical Report CNS-TR- 2010–001.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Caltech-UCSD Birds 200. Technical Report CNS-TR- 2010–001

Reference 34

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This paper cites Deep learning face attributes in the wild.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Deep learning face attributes in the wild

Reference 35

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This paper cites VGGFace2: A dataset for recognising faces across pose and age.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection VGGFace2: A dataset for recognising faces across pose and age

Reference 36

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This paper cites Lung and Colon Cancer Histopathological Image Dataset (LC25000).

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Lung and Colon Cancer Histopathological Image Dataset (LC25000)

Reference 37

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This paper cites Deep inside convolutional networks: visualising image classification models and saliency maps.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Deep inside convolutional networks: visualising image classification models and saliency maps

Reference 38

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This paper cites Defining and extracting generalizable interaction primitives from dnns.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Defining and extracting generalizable interaction primitives from dnns

Reference 39

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This paper cites Grad-CAM++: Generalized gradient-based visual explanations for deep convolutional networks.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Grad-CAM++: Generalized gradient-based visual explanations for deep convolutional networks

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.244591Z

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Observation 5515b902-b374-4ef4-836b-a1904a3d45f5 · outbound

This paper cites Score-CAM: Score-weighted visual explanations for con- volutional neural networks.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Score-CAM: Score-weighted visual explanations for con- volutional neural networks

Reference 41

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This paper cites ViT-CX: causal explanation of vision transformers.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection ViT-CX: causal explanation of vision transformers

Reference 42

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Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection A value for n-person games

Reference 43

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This paper cites Harsanyinet: Computing accurate shapley values in a single forward propagation.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Harsanyinet: Computing accurate shapley values in a single forward propagation

Reference 44

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Observation e60a2e11-a384-4c48-969b-3f07ec94f727 · outbound

This paper cites Problems with shapley-value-based explanations as feature importance measures.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Problems with shapley-value-based explanations as feature importance measures

Reference 45

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Observation 28b40c35-5839-4213-a783-7b53e0042e9e · outbound

This paper cites ”why should i trust you?.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection ”why should i trust you?

Reference 46

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Observation 7b8a4eb2-55c9-4efe-a6aa-ba7a0165ca4b · outbound

This paper cites One explanation is not enough: structured attention graphs for image classification.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection One explanation is not enough: structured attention graphs for image classification

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:32:11.533715Z

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Observation 5f621393-d758-40e6-8e0e-247ccff2695d · outbound

This paper cites Transformer interpretability beyond attention visualization.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Transformer interpretability beyond attention visualization

Reference 48

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Observation 79676844-a8b1-44a5-9b25-d301ac48e5c9 · outbound

This paper cites Vision transformers need registers.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Vision transformers need registers

Reference 49

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Observation 456c4745-f6c4-49de-9521-051e6158d2af · outbound

This paper cites Interpreting clip’s image representation via text-based decomposition.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Interpreting clip’s image representation via text-based decomposition

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:32:11.598503Z

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Observation 40ed767d-675e-4bdf-922a-bf861dfac905 · outbound

This paper cites Discover and cure: Concept-aware mitigation of spurious correlation.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Discover and cure: Concept-aware mitigation of spurious correlation

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:32:11.572616Z

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Observation 0cab297e-bc99-44cb-bdb5-7df7cd82dcae · outbound

This paper cites Meaningfully debugging model mistakes using conceptual counterfactual explanations.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Meaningfully debugging model mistakes using conceptual counterfactual explanations

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.103074Z

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Observation 6b90fe67-1567-4eeb-aecd-09bbd2220443 · outbound

This paper cites Over- looked factors in concept-based explanations: Dataset choice, concept learnability, and human capability.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Over- looked factors in concept-based explanations: Dataset choice, concept learnability, and human capability

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:32:11.557210Z

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Observation a86f0f8e-e706-4aae-b420-eab0d532278f · outbound

This paper cites Talisman: targeted active learning for object detection with rare classes and slices using submodular mutual information.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Talisman: targeted active learning for object detection with rare classes and slices using submodular mutual information

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:32:11.561056Z

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Observation 30882702-1f09-4240-822d-c078e259f4d2 · outbound

This paper cites Ef- ficient modality selection in multimodal learning.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Ef- ficient modality selection in multimodal learning

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:32:11.579373Z

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Observation 137eb0ca-f5b3-4cf0-ab94-19a3f7fadcfb · outbound

This paper cites Marginal contribution feature importance-an axiomatic approach for explaining data.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Marginal contribution feature importance-an axiomatic approach for explaining data

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:32:11.553485Z

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Observation 3491b04d-8836-4cf4-8b3a-10a210c4bb80 · outbound

This paper cites Stream- ing weak submodularity: Interpreting neural networks on the fly.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Stream- ing weak submodularity: Interpreting neural networks on the fly

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:32:11.564621Z

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Observation 4be166d5-bb77-4996-ba09-0c0029a780c1 · outbound

This paper cites Learning to explain: An information-theoretic perspective on model interpretation.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Learning to explain: An information-theoretic perspective on model interpretation

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.125242Z

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Observation d77c7382-1ad7-4c1b-a331-258bf2708fb6 · outbound

This paper cites Scalable subset sampling with neural conditional poisson networks.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Scalable subset sampling with neural conditional poisson networks

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.115701Z

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Observation 7c1ee63e-75e2-48a2-b78a-c026f14d9ae5 · outbound

This paper cites ”what data benefits my classifier?.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection ”what data benefits my classifier?

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:32:11.526312Z

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Observation d70a5300-40d8-4cc3-8da4-fc9e349fb674 · outbound

This paper cites An analysis of approximations for maximizing submodular set functions—i.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection An analysis of approximations for maximizing submodular set functions—i

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:32:11.544903Z

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Observation 6e08fad7-7450-48a7-b195-260e4c820c02 · outbound

This paper cites Lazier than lazy greedy.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Lazier than lazy greedy

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:32:11.529919Z

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Observation 154b9f6d-0d80-4cee-ab0a-3e67ee26ef64 · outbound

This paper cites Submodular batch selection for training deep neural networks.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Submodular batch selection for training deep neural networks

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:32:11.549338Z

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Observation ceeb3197-9e59-4110-b3b8-a2106d63b1eb · outbound

This paper cites Submodular functions, matroids, and certain polyhedra.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Submodular functions, matroids, and certain polyhedra

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.248028Z

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Observation 63c0040d-3af5-4364-b334-741610e56770 · outbound

This paper cites SLIC superpixels compared to state-of-the-art superpixel methods.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection SLIC superpixels compared to state-of-the-art superpixel methods

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.118790Z

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Observation 0484dffd-2bb1-4e70-bf82-ea862a427632 · outbound

This paper cites Segment anything.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Segment anything

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:32:11.540863Z

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Observation d8cf3bc4-074d-4f30-b6ae-f2b7dc08bac1 · outbound

This paper cites Unsupervised learning of visual representa- tions by solving jigsaw puzzles.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Unsupervised learning of visual representa- tions by solving jigsaw puzzles

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.232147Z

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Observation 750416d0-6e12-4ec2-a8dc-b44dc9cbe20b · outbound

This paper cites Learn to threshold: Thresholdnet with confidence-guided manifold mixup for polyp segmentation.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Learn to threshold: Thresholdnet with confidence-guided manifold mixup for polyp segmentation

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.112487Z

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Observation f6ff80ec-9a4d-4991-a37a-1d4359a259a8 · outbound

This paper cites Test-time prompt tuning for zero-shot generalization in vision-language models.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Test-time prompt tuning for zero-shot generalization in vision-language models

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.109480Z

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Observation eb0675ac-4992-4f44-a68b-55cb8fd1c2d2 · outbound

This paper cites Handling open-set noise and novel target recognition in domain adaptive semantic segmentation.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Handling open-set noise and novel target recognition in domain adaptive semantic segmentation

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.121774Z

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Observation 4625c1e5-fa03-4cba-9398-d40083baa602 · outbound

This paper cites Cosface: Large margin cosine loss for deep face recognition.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Cosface: Large margin cosine loss for deep face recognition

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.128765Z

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Observation 4e0b3a86-1ab2-48e8-88b8-848da8a8ccd5 · outbound

This paper cites Arcface: Additive angular margin loss for deep face recognition.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Arcface: Additive angular margin loss for deep face recognition

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.093432Z

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Observation 2a00f337-dd9c-420d-9901-e589033cf66a · outbound

This paper cites Submodularity in data subset selection and active learning.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Submodularity in data subset selection and active learning

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.131805Z

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Observation d833d58c-74dd-4e69-b011-3c940f5fd3a5 · outbound

This paper cites Deep residual learning for image recognition.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Deep residual learning for image recognition

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.256375Z

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Observation e2b712d2-f345-4bab-aac3-b8ba980d40dd · outbound

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

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection An image is worth 16x16 words: Transformers for image recognition at scale

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.253718Z

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Observation 3db24d07-2c9b-46bc-8100-f97b5fcf3626 · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted win- dows.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Swin transformer: Hierarchical vision transformer using shifted win- dows

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.238216Z

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Observation 56414855-a8ad-41fa-b655-64a227b5521b · outbound

This paper cites Vision mamba: Efficient visual representation learning with bidirectional state space model.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Vision mamba: Efficient visual representation learning with bidirectional state space model

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.134940Z

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Observation 8ef70245-d82d-4fe8-ab6d-253ffd0433aa · outbound

This paper cites MambaVision: A Hybrid Mamba-Transformer Vision Backbone.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection MambaVision: A Hybrid Mamba-Transformer Vision Backbone

Reference 78

Resolution: verified exact; arxiv_id, observed 2026-05-22T21:32:10.228915Z

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Observation cda3c5cf-e250-4428-a5ea-716e07c488c5 · outbound

This paper cites MobileNetV2: Inverted residuals and linear bottlenecks.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection MobileNetV2: Inverted residuals and linear bottlenecks

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.096967Z

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Observation dd1acc3e-a9d4-4efe-a89c-7b1506bb7519 · outbound

This paper cites EfficientNetV2: Smaller models and faster training.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection EfficientNetV2: Smaller models and faster training

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.250998Z

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Observation b2ac2a04-04d5-4cdb-95a0-f84bdd465ac1 · outbound

This paper cites Evaluating and aggregating feature-based model explanations.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Evaluating and aggregating feature-based model explanations

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.100590Z

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Observation c8d417b2-c625-4d3a-9569-7b383b1bf031 · outbound

This paper cites Instance-wise or class-wise? a tale of neighbor shapley for concept- based explanation.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Instance-wise or class-wise? a tale of neighbor shapley for concept- based explanation

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.210796Z

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Observation c785a807-3bb9-4ea4-a57d-e0b8256c93e0 · outbound

This paper cites Foundation Models for Music: A Survey.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Foundation Models for Music: A Survey

Reference 83

Resolution: verified exact; arxiv_id, observed 2026-05-22T21:32:10.244241Z

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Observation 2a311df7-4d4f-4af8-9f92-81492c989ac7 · outbound

This paper cites A benchmark for interpretability methods in deep neural networks.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection A benchmark for interpretability methods in deep neural networks

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.213773Z

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Observation 9befa24f-ffe9-46e3-accd-36447c67d6f7 · outbound

This paper cites Not Just a Black Box: Learning Important Features Through Propagating Activation Differences.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Not Just a Black Box: Learning Important Features Through Propagating Activation Differences

Reference 85

Resolution: verified exact; local_arxiv, observed 2026-05-22T21:32:10.222972Z

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Observation 7747c379-9ad9-4930-b229-ac72b6b987a1 · outbound

This paper cites SmoothGrad: removing noise by adding noise.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection SmoothGrad: removing noise by adding noise

Reference 86

Resolution: verified exact; local_arxiv, observed 2026-05-22T21:32:10.238742Z

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Observation 166444cd-236e-46c6-ba1d-3c9c30dce843 · outbound

This paper cites Noise-adding Methods of Saliency Map as Series of Higher Order Partial Derivative.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Noise-adding Methods of Saliency Map as Series of Higher Order Partial Derivative

Reference 87

Resolution: verified exact; local_arxiv, observed 2026-05-22T21:32:10.233963Z

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Observation 397d0538-c9bc-44ed-8571-b19ae105156d · outbound

This paper cites Towards better understanding of gradient-based attribution methods for deep neural networks.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Towards better understanding of gradient-based attribution methods for deep neural networks

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.090543Z

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Observation eb8ee13f-a472-4ad0-89ac-73aae56c661a · outbound

This paper cites Look at the variance! efficient black-box explanations with sobol-based sensitivity analysis.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Look at the variance! efficient black-box explanations with sobol-based sensitivity analysis

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.086945Z

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Observation a1e2185d-e938-4e19-a829-fb24ddfe777f · outbound

This paper cites Explaining nonlinear classification decisions with deep taylor decom- position.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Explaining nonlinear classification decisions with deep taylor decom- position

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.083385Z

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Observation 4cda6c95-fc1e-45f4-92d1-e612ea62363a · outbound

This paper cites We compare with RISE [23] and HSIC- Attribution [9] methods, showing the advantages of our method.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection We compare with RISE [23] and HSIC- Attribution [9] methods, showing the advantages of our method

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T21:35:13.225436Z

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Observation cc6dfd06-b1da-4693-bc1a-d29d61150ed8 · outbound

This paper cites an unresolved cited work.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Unresolved cited work

Reference 92

Resolution: unresolved; raw_fallback, observed 2026-05-22T21:35:13.276567Z

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Observation 66a7f515-bed5-4875-a6a8-e500e966873d · outbound

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Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Unresolved cited work

Reference 93

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Observation 47d5b4a2-4a7c-4cbc-a565-70c82f9d85e4 · outbound

This paper cites an unresolved cited work.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Unresolved cited work

Reference 94

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Observation ad3242e7-0ad0-4d51-954e-f7b02868884d · outbound

This paper cites an unresolved cited work.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Unresolved cited work

Reference 95

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Observation 59e9aec0-9aba-4cd2-9e07-f859d45b67d0 · outbound

This paper cites an unresolved cited work.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Unresolved cited work

Reference 96

Resolution: unresolved; raw_fallback, observed 2026-05-22T21:35:13.261750Z

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Observation 155b546e-32eb-40d9-9074-731192cd4a5f · outbound

This paper cites an unresolved cited work.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Unresolved cited work

Reference 97

Resolution: unresolved; raw_fallback, observed 2026-05-22T21:35:13.259146Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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