Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions | Paper Citation Record

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This paper cites Sanity checks for saliency maps,.

Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Sanity checks for saliency maps,

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This paper cites Logic Traps in Evaluating Attribution Scores.

Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Logic Traps in Evaluating Attribution Scores

Reference 2

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This paper cites Evaluation of post-hoc interpretability methods in time- series classification,.

Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Evaluation of post-hoc interpretability methods in time- series classification,

Reference 3

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This paper cites Saliency methods are encoders: Analysing logical relations towards interpreta- tion,.

Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Saliency methods are encoders: Analysing logical relations towards interpreta- tion,

Reference 4

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This paper cites Constructing global coherence representations: Identifying interpretability and coherences of transformer attention in time series data,.

Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Constructing global coherence representations: Identifying interpretability and coherences of transformer attention in time series data,

Reference 5

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This paper cites Abstracting local transformer attention for enhancing interpretability on time series data.,.

Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Abstracting local transformer attention for enhancing interpretability on time series data.,

Reference 6

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This paper cites Extracting Interpretable Local and Global Representations from Attention on Time Series.

Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Extracting Interpretable Local and Global Representations from Attention on Time Series

Reference 7

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This paper cites Molnar, Interpretable machine learning.

Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Molnar, Interpretable machine learning

Reference 8

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This paper cites Joint Shapley values: a measure of joint feature importance.

Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Joint Shapley values: a measure of joint feature importance

Reference 9

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This paper cites Shapley residuals: Quantifying the limits of the shapley value for explanations,.

Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Shapley residuals: Quantifying the limits of the shapley value for explanations,

Reference 10

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This paper cites Faith-shap: The faithful shapley interaction index,.

Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Faith-shap: The faithful shapley interaction index,

Reference 11

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This paper cites Disentangling Interactions and Dependencies in Feature Attribution.

Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Disentangling Interactions and Dependencies in Feature Attribution

Reference 12

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This paper cites Shap-iq: Unified approximation of any-order shapley interactions,.

Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Shap-iq: Unified approximation of any-order shapley interactions,

Reference 13

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This paper cites Interpreting multivariate shapley interactions in dnns,.

Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Interpreting multivariate shapley interactions in dnns,

Reference 14

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This paper cites How does this interaction affect me? interpretable attribution for feature interactions,.

Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions How does this interaction affect me? interpretable attribution for feature interactions,

Reference 15

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This paper cites Explaining explanations: Axiomatic feature interactions for deep net- works,.

Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Explaining explanations: Axiomatic feature interactions for deep net- works,

Reference 16

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This paper cites Ma- chine learning interpretability: A survey on methods and metrics,.

Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Ma- chine learning interpretability: A survey on methods and metrics,

Reference 18

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This paper cites An experimental study of quantitative evaluations on saliency methods,.

Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions An experimental study of quantitative evaluations on saliency methods,

Reference 19

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This paper cites The (un) reliability of saliency methods,.

Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions The (un) reliability of saliency methods,

Reference 20

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This paper cites Do input gradients highlight discriminative features?,.

Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Do input gradients highlight discriminative features?,

Reference 21

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This paper cites Investigating sanity checks for saliency maps with image and text classification.

Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Investigating sanity checks for saliency maps with image and text classification

Reference 22

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This paper cites When explanations lie: Why many modified bp attributions fail,.

Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions When explanations lie: Why many modified bp attributions fail,

Reference 23

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This paper cites Towards Faithfully Interpretable NLP Systems: How should we define and evaluate faithfulness?.

Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Towards Faithfully Interpretable NLP Systems: How should we define and evaluate faithfulness?

Reference 24

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This paper cites On Baselines for Local Feature Attributions.

Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions On Baselines for Local Feature Attributions

Reference 25

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This paper cites Visualizing the impact of feature attribution baselines,.

Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Visualizing the impact of feature attribution baselines,

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

Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions A benchmark for interpretability methods in deep neural networks,

Reference 27

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

Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions A Consistent and Efficient Evaluation Strategy for Attribution Methods

Reference 28

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This paper cites Geometric remove-and-retrain (goar): Coordinate-invariant explain- able ai assessment,.

Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Geometric remove-and-retrain (goar): Coordinate-invariant explain- able ai assessment,

Reference 29

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Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Learning global pairwise interactions with bayesian neural networks,

Reference 30

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Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Fooling lime and shap: Adversarial attacks on post hoc explanation methods,

Reference 31

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This paper cites Shapley values for feature selection: The good, the bad, and the axioms,.

Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Shapley values for feature selection: The good, the bad, and the axioms,

Reference 32

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This paper cites Show me what you’re looking for: Visualizing abstracted transformer attention for enhancing their local interpretability on time series data,.

Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Show me what you’re looking for: Visualizing abstracted transformer attention for enhancing their local interpretability on time series data,

Reference 33

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This paper cites A survey on neural network interpretability,.

Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions A survey on neural network interpretability,

Reference 34

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This paper cites Logic-based explainability in machine learning,.

Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Logic-based explainability in machine learning,

Reference 35

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Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Revisit fuzzy neural network: bridging the gap between fuzzy logic and deep learning,

Reference 36

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Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Neural-symbolic learning and reasoning: A survey and interpretation,

Reference 37

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This paper cites Logical expla- nations for deep relational machines using relevance information,.

Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Logical expla- nations for deep relational machines using relevance information,

Reference 38

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This paper cites Meaningful explanations of black box ai decision systems,.

Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Meaningful explanations of black box ai decision systems,

Reference 39

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This paper cites Evaluating the Correctness of Explainable AI Algorithms for Classification.

Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Evaluating the Correctness of Explainable AI Algorithms for Classification

Reference 40

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This paper cites Evaluation of post-hoc xai approaches through synthetic tabular data,.

Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Evaluation of post-hoc xai approaches through synthetic tabular data,

Reference 41

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This paper cites Evaluating feature attribution: An information-theoretic perspective,.

Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Evaluating feature attribution: An information-theoretic perspective,

Reference 42

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This paper cites Sanity checks for saliency metrics,.

Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Sanity checks for saliency metrics,

Reference 43

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This paper cites Metrics for saliency map evaluation of deep learning explanation methods,.

Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Metrics for saliency map evaluation of deep learning explanation methods,

Reference 44

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This paper cites Explaining deep neural networks: A survey on the global interpretation methods,.

Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Explaining deep neural networks: A survey on the global interpretation methods,

Reference 45

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This paper cites A Symbolic Representation of Time Series, with Implications for Streaming Algorithms,.

Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions A Symbolic Representation of Time Series, with Implications for Streaming Algorithms,

Reference 46

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This paper cites Experiencing SAX: A Novel Symbolic Representation of Time Series,.

Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Experiencing SAX: A Novel Symbolic Representation of Time Series,

Reference 47

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This paper cites Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Survey.

Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Survey

Reference 48

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This paper cites Explanation-aware feature selection using symbolic time series abstraction: approaches and experiences in a petro-chemical production context,.

Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Explanation-aware feature selection using symbolic time series abstraction: approaches and experiences in a petro-chemical production context,

Reference 49

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This paper cites Imbalanced-learn: A python toolbox to tackle the curse of imbalanced datasets in machine learning,.

Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Imbalanced-learn: A python toolbox to tackle the curse of imbalanced datasets in machine learning,

Reference 50

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This paper cites Transformers in Time Series: A Survey.

Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Transformers in Time Series: A Survey

Reference 51

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

Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Grad-cam: Visual explanations from deep networks via gradient-based localization,

Reference 52

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Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Deep residual learning for image recognition,

Reference 53

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Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Attention is all you need,

Reference 54

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Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Transformer inter- pretability beyond attention visualization,

Reference 55

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Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation,

Reference 56

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Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Quantifying Attention Flow in Transformers

Reference 57

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Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Axiomatic at- tribution for deep networks,

Reference 58

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Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Captum: A unified and generic model interpretability library for PyTorch

Reference 59

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Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Learning important features through propagating activation differ- ences,

Reference 60

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Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Visualizing and understand- ing convolutional networks,

Reference 61

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Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Pytorch library for cam methods

Reference 62

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Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Grad-cam++: Generalized gradient- based visual explanations for deep convolutional net- works,

Reference 63

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Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions A unified approach to interpreting model predictions,

Reference 64

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Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions On the effects of non-normality on the distribution of the sample product-moment correlation coefficient,

Reference 65

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Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Random forests,

Reference 66

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Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Unmasking clever hans predictors and assessing what machines really learn,

Reference 67

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Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Revisiting Sanity Checks for Saliency Maps

Reference 68

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