Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-11T13:54:19.368008Z
Paper Citation Record · LEDGER
Saliency Methods are Encoders: Analysing Logical Relations Towards Interpretation
As of 12 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:2412.16204.
A citation records a reference. It does not transfer a finding from one paper to another.
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One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
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Source: cited_works
Reference resolution
50 of 50 outbound references displayed
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External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 26cd8a8f-2267-423a-abe1-0f12fa1f7a77 · outbound
This paper cites Quantifying Attention Flow in Transformers.
Saliency Methods are Encoders: Analysing Logical Relations Towards Interpretation Quantifying Attention Flow in Transformers
Reference 1
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This paper cites Sanity checks for saliency maps.
Saliency Methods are Encoders: Analysing Logical Relations Towards Interpretation Sanity checks for saliency maps
Reference 2
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Observation 2d1e176f-2a31-4012-acce-3a0eb2b3e9b5 · outbound
This paper cites On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation.
Saliency Methods are Encoders: Analysing Logical Relations Towards Interpretation On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Reference 3
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This paper cites Random forests.
Saliency Methods are Encoders: Analysing Logical Relations Towards Interpretation Random forests
Reference 4
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Observation 48a58cac-d8ed-455d-9502-a8d859d38f5d · outbound
This paper cites Machine learning interpretability: A survey on meth- ods and metrics.
Saliency Methods are Encoders: Analysing Logical Relations Towards Interpretation Machine learning interpretability: A survey on meth- ods and metrics
Reference 5
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This paper cites Grad-cam++: Gener- alized gradient-based visual explanations for deep convo- lutional networks.
Saliency Methods are Encoders: Analysing Logical Relations Towards Interpretation Grad-cam++: Gener- alized gradient-based visual explanations for deep convo- lutional networks
Reference 6
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Observation 65213caf-f9f7-4c7b-84d2-1974b85824a5 · outbound
This paper cites Transformer inter- pretability beyond attention visualization.
Saliency Methods are Encoders: Analysing Logical Relations Towards Interpretation Transformer inter- pretability beyond attention visualization
Reference 7
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Observation c0dbb652-7003-44d1-9c71-84607db3e2d8 · outbound
This paper cites Robust physical-world attacks on deep learning visual classification.
Saliency Methods are Encoders: Analysing Logical Relations Towards Interpretation Robust physical-world attacks on deep learning visual classification
Reference 8
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This paper cites Revisit fuzzy neural network: bridging the gap between fuzzy logic and deep learning.
Saliency Methods are Encoders: Analysing Logical Relations Towards Interpretation Revisit fuzzy neural network: bridging the gap between fuzzy logic and deep learning
Reference 9
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Observation e8d9576c-37a5-4200-8279-3ce9865e51d5 · outbound
This paper cites Shapley values for feature selection: The good, the bad, and the axioms.
Saliency Methods are Encoders: Analysing Logical Relations Towards Interpretation Shapley values for feature selection: The good, the bad, and the axioms
Reference 10
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Observation 67dd640e-3266-4235-8a70-d6495c3d92be · outbound
This paper cites Neural- symbolic learning and reasoning: A survey and interpreta- tion.
Saliency Methods are Encoders: Analysing Logical Relations Towards Interpretation Neural- symbolic learning and reasoning: A survey and interpreta- tion
Reference 11
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Observation 3ff0b269-9d0b-4d17-ac2f-94af4c3eb630 · outbound
This paper cites Pytorch library for cam methods.
Saliency Methods are Encoders: Analysing Logical Relations Towards Interpretation Pytorch library for cam methods
Reference 12
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This paper cites Joint Shapley values: a measure of joint feature importance.
Saliency Methods are Encoders: Analysing Logical Relations Towards Interpretation Joint Shapley values: a measure of joint feature importance
Reference 13
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This paper cites On Baselines for Local Feature Attributions.
Saliency Methods are Encoders: Analysing Logical Relations Towards Interpretation On Baselines for Local Feature Attributions
Reference 14
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Source-reported events for the cited work
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Observation a62225f8-7b2e-4552-942f-ba5437aa45b1 · outbound
This paper cites Deep residual learning for image recognition.
Saliency Methods are Encoders: Analysing Logical Relations Towards Interpretation Deep residual learning for image recognition
Reference 15
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This paper cites A benchmark for interpretability methods in deep neural networks.
Saliency Methods are Encoders: Analysing Logical Relations Towards Interpretation A benchmark for interpretability methods in deep neural networks
Reference 16
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Observation af8ff131-9730-4bfa-868c-d3e560fe9c88 · outbound
This paper cites Unre- stricted permutation forces extrapolation: variable impor- tance requires at least one more model, or there is no free variable importance.
Saliency Methods are Encoders: Analysing Logical Relations Towards Interpretation Unre- stricted permutation forces extrapolation: variable impor- tance requires at least one more model, or there is no free variable importance
Reference 17
- Resolution
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Observation d8dd5ced-db62-426d-9194-bad775103c6e · outbound
This paper cites Towards Faithfully Interpretable NLP Systems: How should we define and evaluate faithfulness?.
Saliency Methods are Encoders: Analysing Logical Relations Towards Interpretation Towards Faithfully Interpretable NLP Systems: How should we define and evaluate faithfulness?
Reference 18
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Observation e01b8e95-cedc-4137-b4f8-e67bf4d61cbe · outbound
This paper cites Logic Traps in Evaluating Attribution Scores.
Saliency Methods are Encoders: Analysing Logical Relations Towards Interpretation Logic Traps in Evaluating Attribution Scores
Reference 19
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This paper cites The (un) reliability of saliency methods.
Saliency Methods are Encoders: Analysing Logical Relations Towards Interpretation The (un) reliability of saliency methods
Reference 20
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This paper cites Captum: A unified and generic model interpretability library for PyTorch.
Saliency Methods are Encoders: Analysing Logical Relations Towards Interpretation Captum: A unified and generic model interpretability library for PyTorch
Reference 21
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Observation cf6c1418-e7d2-471b-a6e1-9fedca1401b6 · outbound
This paper cites Investigating sanity checks for saliency maps with image and text classification.
Saliency Methods are Encoders: Analysing Logical Relations Towards Interpretation Investigating sanity checks for saliency maps with image and text classification
Reference 22
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This paper cites Shapley residuals: Quantifying the limits of the shapley value for explana- tions.
Saliency Methods are Encoders: Analysing Logical Relations Towards Interpretation Shapley residuals: Quantifying the limits of the shapley value for explana- tions
Reference 23
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Saliency Methods are Encoders: Analysing Logical Relations Towards Interpretation Unmasking clever hans predictors and as- sessing what machines really learn
Reference 24
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Saliency Methods are Encoders: Analysing Logical Relations Towards Interpretation Unresolved cited work
Reference 25
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This paper cites An experimental study of quantitative evaluations on saliency methods.
Saliency Methods are Encoders: Analysing Logical Relations Towards Interpretation An experimental study of quantitative evaluations on saliency methods
Reference 26
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Saliency Methods are Encoders: Analysing Logical Relations Towards Interpretation A unified approach to interpreting model predictions
Reference 27
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Saliency Methods are Encoders: Analysing Logical Relations Towards Interpretation Logic-based explainability in ma- chine learning
Reference 28
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Saliency Methods are Encoders: Analysing Logical Relations Towards Interpretation Interpretable machine learning
Reference 29
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Saliency Methods are Encoders: Analysing Logical Relations Towards Interpretation Meaningful explanations of black box ai decision systems
Reference 30
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This paper cites Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Survey.
Saliency Methods are Encoders: Analysing Logical Relations Towards Interpretation Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Survey
Reference 31
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Saliency Methods are Encoders: Analysing Logical Relations Towards Interpretation A Consistent and Efficient Evaluation Strategy for Attribution Methods
Reference 32
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Saliency Methods are Encoders: Analysing Logical Relations Towards Interpretation Evaluating feature attribution: An information-theoretic perspective
Reference 33
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Saliency Methods are Encoders: Analysing Logical Relations Towards Interpretation Learning interpretable models
Reference 34
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Saliency Methods are Encoders: Analysing Logical Relations Towards Interpretation Grad-cam: Visual explanations from deep networks via gradient-based localization
Reference 35
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This paper cites Do input gradients highlight discriminative features? Advances in Neural Information Processing Systems, 34:2046–2059, 2021.
Saliency Methods are Encoders: Analysing Logical Relations Towards Interpretation Do input gradients highlight discriminative features? Advances in Neural Information Processing Systems, 34:2046–2059, 2021
Reference 36
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Saliency Methods are Encoders: Analysing Logical Relations Towards Interpretation Learning important features through propagating acti- vation differences
Reference 37
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Saliency Methods are Encoders: Analysing Logical Relations Towards Interpretation When explanations lie: Why many modified bp attributions fail
Reference 38
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Saliency Methods are Encoders: Analysing Logical Relations Towards Interpretation Fooling lime and shap: Ad- versarial attacks on post hoc explanation methods
Reference 39
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Saliency Methods are Encoders: Analysing Logical Relations Towards Interpretation Logical explanations for deep relational machines using relevance information
Reference 40
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Saliency Methods are Encoders: Analysing Logical Relations Towards Interpretation Visu- alizing the impact of feature attribution baselines
Reference 41
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Saliency Methods are Encoders: Analysing Logical Relations Towards Interpretation Ax- iomatic attribution for deep networks
Reference 42
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Saliency Methods are Encoders: Analysing Logical Relations Towards Interpretation Sanity checks for saliency metrics
Reference 43
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Saliency Methods are Encoders: Analysing Logical Relations Towards Interpretation Evaluation of post-hoc xai ap- proaches through synthetic tabular data
Reference 44
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Saliency Methods are Encoders: Analysing Logical Relations Towards Interpretation Attention is all you need
Reference 45
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Saliency Methods are Encoders: Analysing Logical Relations Towards Interpretation Transformers in Time Series: A Survey
Reference 46
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Saliency Methods are Encoders: Analysing Logical Relations Towards Interpretation Evaluating the Correctness of Explainable AI Algorithms for Classification
Reference 47
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Saliency Methods are Encoders: Analysing Logical Relations Towards Interpretation Revisiting Sanity Checks for Saliency Maps
Reference 48
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Saliency Methods are Encoders: Analysing Logical Relations Towards Interpretation Visualizing and un- derstanding convolutional networks
Reference 49
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Saliency Methods are Encoders: Analysing Logical Relations Towards Interpretation A survey on neural network interpretability
Reference 50
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Pith citing papers
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