Paper Citation Record · LEDGER

ABE: A Unified Framework for Robust and Faithful Attribution-Based Explainability

As of 20 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2505.06258.

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

pith.paper-citation-record.v1
2505.06258 v1

Coverage vector

measured 44 of 44 reference resolution

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measured 44 of 44 standing notices

One-hop event checks from named stored sources.

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

44 of 44 outbound references displayed

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External citation measurements

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Outbound references

Observation 7c71cdce-e182-41e1-bc1b-8130d874a3b6 · outbound

This paper cites Evaluating the quality of machine learning explanations: A survey on methods and metrics,.

ABE: A Unified Framework for Robust and Faithful Attribution-Based Explainability Evaluating the quality of machine learning explanations: A survey on methods and metrics,

Reference 1

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Observation 69ca1a7f-a085-4c4f-a4b5-f9e3b2caefb9 · outbound

This paper cites Axiomatic attribution for deep networks,.

ABE: A Unified Framework for Robust and Faithful Attribution-Based Explainability Axiomatic attribution for deep networks,

Reference 2

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Observation 633a93cc-1faa-4d6d-a8d6-d96de5bca032 · outbound

This paper cites Robust Models Are More Interpretable Because Attributions Look Normal.

ABE: A Unified Framework for Robust and Faithful Attribution-Based Explainability Robust Models Are More Interpretable Because Attributions Look Normal

Reference 3

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

ABE: A Unified Framework for Robust and Faithful Attribution-Based Explainability Grad-cam: Visual explanations from deep networks via gradient-based localization,

Reference 4

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Observation d7646b40-2ab4-43f8-98d4-026d24be1efc · outbound

This paper cites Explaining deep neural network models with adversarial gradient integration,.

ABE: A Unified Framework for Robust and Faithful Attribution-Based Explainability Explaining deep neural network models with adversarial gradient integration,

Reference 5

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Observation 260ab4f5-1a77-4750-87c7-a3f36db5c3eb · outbound

This paper cites Mfaba: A more faithful and accelerated boundary-based attribution method for deep neural networks,.

ABE: A Unified Framework for Robust and Faithful Attribution-Based Explainability Mfaba: A more faithful and accelerated boundary-based attribution method for deep neural networks,

Reference 6

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Observation 3f48aeb2-86b6-4003-b1d8-4fa0305691b8 · outbound

This paper cites Attexplore: Attribution for explanation with model parameters exploration,.

ABE: A Unified Framework for Robust and Faithful Attribution-Based Explainability Attexplore: Attribution for explanation with model parameters exploration,

Reference 7

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Observation a5223835-43ad-4230-950f-04c862e8ecc6 · outbound

This paper cites A unified approach to interpreting model predictions,.

ABE: A Unified Framework for Robust and Faithful Attribution-Based Explainability A unified approach to interpreting model predictions,

Reference 8

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Observation fc3e7b1d-80b6-426c-8c39-16cc941ad133 · outbound

This paper cites OmniXAI: A Library for Explainable AI.

ABE: A Unified Framework for Robust and Faithful Attribution-Based Explainability OmniXAI: A Library for Explainable AI

Reference 9

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Observation b408befb-afda-42d8-9e8d-8613b58ba3c5 · outbound

This paper cites Interpretdl: explaining deep models in paddlepaddle,.

ABE: A Unified Framework for Robust and Faithful Attribution-Based Explainability Interpretdl: explaining deep models in paddlepaddle,

Reference 10

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Observation abcaf9fb-b468-4630-803f-9032aeec6cba · outbound

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

ABE: A Unified Framework for Robust and Faithful Attribution-Based Explainability Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 11

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Observation aa3249b9-82fd-4f09-8cf5-7be713bd78b7 · outbound

This paper cites SmoothGrad: removing noise by adding noise.

ABE: A Unified Framework for Robust and Faithful Attribution-Based Explainability SmoothGrad: removing noise by adding noise

Reference 12

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Observation f31a4e47-50e1-4409-8a73-d0ef9ffecf0f · outbound

This paper cites Layer-wise relevance propagation for neural networks with local renor- malization layers,.

ABE: A Unified Framework for Robust and Faithful Attribution-Based Explainability Layer-wise relevance propagation for neural networks with local renor- malization layers,

Reference 13

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This paper cites Learning important features through propagating activation differences,.

ABE: A Unified Framework for Robust and Faithful Attribution-Based Explainability Learning important features through propagating activation differences,

Reference 14

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Observation d59eff8b-ea9b-4f45-904d-0a3334df70fe · outbound

This paper cites A unified approach to interpreting model predictions,.

ABE: A Unified Framework for Robust and Faithful Attribution-Based Explainability A unified approach to interpreting model predictions,

Reference 15

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Observation 17686713-1302-4733-9b60-2f1a468968a4 · outbound

This paper cites ” why should i trust you?.

ABE: A Unified Framework for Robust and Faithful Attribution-Based Explainability ” why should i trust you?

Reference 16

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Observation 1c336c06-f42b-4bc5-8ae2-4339eb51a206 · outbound

This paper cites Captum: A unified and generic model inter- pretability library for pytorch,.

ABE: A Unified Framework for Robust and Faithful Attribution-Based Explainability Captum: A unified and generic model inter- pretability library for pytorch,

Reference 17

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Observation bfdcda57-ee4e-412c-b35b-6c13e3e81a3e · outbound

This paper cites dattri: A library for efficient data attribution,.

ABE: A Unified Framework for Robust and Faithful Attribution-Based Explainability dattri: A library for efficient data attribution,

Reference 18

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Observation e14df881-9a58-4967-aefa-99a96e871fad · outbound

This paper cites InterpretML: A Unified Framework for Machine Learning Interpretability.

ABE: A Unified Framework for Robust and Faithful Attribution-Based Explainability InterpretML: A Unified Framework for Machine Learning Interpretability

Reference 19

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Observation 0631a8a9-5a6f-453b-ac55-bc651e3e1306 · outbound

This paper cites Interpretdl: Explaining deep models in paddlepaddle,.

ABE: A Unified Framework for Robust and Faithful Attribution-Based Explainability Interpretdl: Explaining deep models in paddlepaddle,

Reference 20

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This paper cites Omnixai: A library for explainable ai,.

ABE: A Unified Framework for Robust and Faithful Attribution-Based Explainability Omnixai: A library for explainable ai,

Reference 21

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Observation 047b0145-8fe3-4a15-b96b-258e5215f7bf · outbound

This paper cites Improving performance of deep learning models with axiomatic attribution priors and expected gradients,.

ABE: A Unified Framework for Robust and Faithful Attribution-Based Explainability Improving performance of deep learning models with axiomatic attribution priors and expected gradients,

Reference 22

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This paper cites Fast axiomatic attribution for neural networks,.

ABE: A Unified Framework for Robust and Faithful Attribution-Based Explainability Fast axiomatic attribution for neural networks,

Reference 23

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This paper cites Guided integrated gradients: An adaptive path method for removing noise,.

ABE: A Unified Framework for Robust and Faithful Attribution-Based Explainability Guided integrated gradients: An adaptive path method for removing noise,

Reference 24

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Observation 726333a4-85c5-4eac-8e72-fc131f46779d · outbound

This paper cites Adversarial examples in the physical world,.

ABE: A Unified Framework for Robust and Faithful Attribution-Based Explainability Adversarial examples in the physical world,

Reference 25

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Observation 008e3f68-636a-45e5-b784-3749d3d4bb4d · outbound

This paper cites Impossibility theo- rems for feature attribution,.

ABE: A Unified Framework for Robust and Faithful Attribution-Based Explainability Impossibility theo- rems for feature attribution,

Reference 26

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

ABE: A Unified Framework for Robust and Faithful Attribution-Based Explainability Rise: Randomized input sampling for explanation of black-box models,

Reference 27

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Observation b6d4782b-0179-4395-be7c-182209115fa1 · outbound

This paper cites Enhancing model interpretability with local attribution over global exploration,.

ABE: A Unified Framework for Robust and Faithful Attribution-Based Explainability Enhancing model interpretability with local attribution over global exploration,

Reference 28

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Observation dd4d70a2-411a-47ca-ac3f-4e4c08434fb5 · outbound

This paper cites Iterative search attribution for deep neural networks,.

ABE: A Unified Framework for Robust and Faithful Attribution-Based Explainability Iterative search attribution for deep neural networks,

Reference 29

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This paper cites Generic attention-model explain- ability for interpreting bi-modal and encoder-decoder transformers,.

ABE: A Unified Framework for Robust and Faithful Attribution-Based Explainability Generic attention-model explain- ability for interpreting bi-modal and encoder-decoder transformers,

Reference 30

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This paper cites Visual explanations of image-text representations via multi-modal information bottleneck attri- bution,.

ABE: A Unified Framework for Robust and Faithful Attribution-Based Explainability Visual explanations of image-text representations via multi-modal information bottleneck attri- bution,

Reference 31

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ABE: A Unified Framework for Robust and Faithful Attribution-Based Explainability Iterative search attribution for deep neural networks,

Reference 32

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ABE: A Unified Framework for Robust and Faithful Attribution-Based Explainability Rethinking the inception architecture for computer vision,

Reference 33

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ABE: A Unified Framework for Robust and Faithful Attribution-Based Explainability Deep residual learning for image recognition,

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ABE: A Unified Framework for Robust and Faithful Attribution-Based Explainability Very Deep Convolutional Networks for Large-Scale Image Recognition

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This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

ABE: A Unified Framework for Robust and Faithful Attribution-Based Explainability An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 36

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ABE: A Unified Framework for Robust and Faithful Attribution-Based Explainability ImageNet Large Scale Visual Recognition Challenge,

Reference 37

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This paper cites A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification.

ABE: A Unified Framework for Robust and Faithful Attribution-Based Explainability A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification

Reference 38

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This paper cites Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling.

ABE: A Unified Framework for Robust and Faithful Attribution-Based Explainability Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

Reference 39

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ABE: A Unified Framework for Robust and Faithful Attribution-Based Explainability Learning transferable visual models from natural language supervision,

Reference 40

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This paper cites Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning,.

ABE: A Unified Framework for Robust and Faithful Attribution-Based Explainability Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning,

Reference 41

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This paper cites Framing image description as a ranking task: Data, models and evaluation metrics,.

ABE: A Unified Framework for Robust and Faithful Attribution-Based Explainability Framing image description as a ranking task: Data, models and evaluation metrics,

Reference 42

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ABE: A Unified Framework for Robust and Faithful Attribution-Based Explainability Focal Loss for Dense Object Detection

Reference 43

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This paper cites Microsoft coco: Common objects in context,.

ABE: A Unified Framework for Robust and Faithful Attribution-Based Explainability Microsoft coco: Common objects in context,

Reference 44

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