Value bounds and Convergence Analysis for Averages of LRP attributions | Paper Citation Record

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Value bounds and Convergence Analysis for Averages of LRP attributions Deep inside convolutional networks: Visualising image classification models and saliency maps,

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Value bounds and Convergence Analysis for Averages of LRP attributions Striving for simplicity: The all convolutional net,

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Value bounds and Convergence Analysis for Averages of LRP attributions Grad-cam: Visual explanations from deep networks via gradient-based localization,

Reference 3

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Value bounds and Convergence Analysis for Averages of LRP attributions Learning important features through propagating activation differences,

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Value bounds and Convergence Analysis for Averages of LRP attributions The shattered gradients problem: If resnets are the answer, then what is the question?,

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Value bounds and Convergence Analysis for Averages of LRP attributions On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation,

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Value bounds and Convergence Analysis for Averages of LRP attributions Unmasking clever hans predictors and assessing what machines really learn,

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Value bounds and Convergence Analysis for Averages of LRP attributions Shortcomings of top-down randomization-based sanity checks for evaluations of deep neural network explanations,

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Value bounds and Convergence Analysis for Averages of LRP attributions AttnLRP: Attention-aware layer-wise relevance propagation for transformers,

Reference 9

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Value bounds and Convergence Analysis for Averages of LRP attributions Mambalrp: Explaining selective state space sequence models,

Reference 10

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Value bounds and Convergence Analysis for Averages of LRP attributions Sanity checks for saliency maps,

Reference 11

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Value bounds and Convergence Analysis for Averages of LRP attributions Towards better understanding of gradient-based attribution methods for deep neural networks,

Reference 12

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Value bounds and Convergence Analysis for Averages of LRP attributions Axiomatic attribution for deep networks,

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Value bounds and Convergence Analysis for Averages of LRP attributions SmoothGrad: removing noise by adding noise

Reference 14

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Value bounds and Convergence Analysis for Averages of LRP attributions Smoothlrp: Smoothing LRP by averaging over stochastic input variations,

Reference 15

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Value bounds and Convergence Analysis for Averages of LRP attributions Explaining prediction models and individual predictions with feature contributions,

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Value bounds and Convergence Analysis for Averages of LRP attributions A unified approach to interpreting model predictions,

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Value bounds and Convergence Analysis for Averages of LRP attributions RISE: randomized input sampling for explanation of black-box models,

Reference 18

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Value bounds and Convergence Analysis for Averages of LRP attributions Interpretable explanations of black boxes by meaningful perturbation,

Reference 19

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Value bounds and Convergence Analysis for Averages of LRP attributions Explaining image classifiers by removing input features using generative models,

Reference 20

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Value bounds and Convergence Analysis for Averages of LRP attributions Towards robust interpretability with self-explaining neural networks,

Reference 21

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Value bounds and Convergence Analysis for Averages of LRP attributions Evaluating the visualization of what a deep neural network has learned,

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Value bounds and Convergence Analysis for Averages of LRP attributions On the (in)fidelity and sensitivity of explanations,

Reference 23

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Value bounds and Convergence Analysis for Averages of LRP attributions Evaluating and aggregating feature-based model explanations,

Reference 24

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Value bounds and Convergence Analysis for Averages of LRP attributions IROF: a low resource evaluation metric for explanation methods

Reference 25

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Value bounds and Convergence Analysis for Averages of LRP attributions On quantitative aspects of model interpretability

Reference 26

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Value bounds and Convergence Analysis for Averages of LRP attributions Framework for evaluating faithfulness of local explanations,

Reference 27

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Value bounds and Convergence Analysis for Averages of LRP attributions Rethinking Stability for Attribution-based Explanations

Reference 28

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Value bounds and Convergence Analysis for Averages of LRP attributions A consistent and efficient evaluation strategy for attribution methods,

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Value bounds and Convergence Analysis for Averages of LRP attributions Sanity Checks Revisited: An Exploration to Repair the Model Parameter Randomisation Test

Reference 30

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Value bounds and Convergence Analysis for Averages of LRP attributions Funnybirds: A synthetic vision dataset for a part-based analysis of explainable AI methods,

Reference 31

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Value bounds and Convergence Analysis for Averages of LRP attributions Benchmarking the attribution quality of vision models,

Reference 32

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Value bounds and Convergence Analysis for Averages of LRP attributions Which explanation should i choose? a function approximation perspective to characterizing post hoc explanations,

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Value bounds and Convergence Analysis for Averages of LRP attributions Initialization noise in image gradients and saliency maps,

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Value bounds and Convergence Analysis for Averages of LRP attributions Towards the unification and robustness of perturbation and gradient based explanations,

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Value bounds and Convergence Analysis for Averages of LRP attributions Rethinking the principle of gradient smooth methods in model explanation,

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Value bounds and Convergence Analysis for Averages of LRP attributions Probabilistic lipschitzness and the stable rank for comparing explanation models,

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Value bounds and Convergence Analysis for Averages of LRP attributions Test-time image-to-image translation ensembling improves out-of-distribution generalization in histopathology,

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Value bounds and Convergence Analysis for Averages of LRP attributions Ai-based anomaly detection for clinical-grade histopathological diagnostics,

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Value bounds and Convergence Analysis for Averages of LRP attributions Improved domain generalization for cell detection in histopathology images via test-time stain augmentation,

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Value bounds and Convergence Analysis for Averages of LRP attributions Fine-tuned densenet-169 for breast cancer metastasis prediction using fastai and 1-cycle policy,

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Value bounds and Convergence Analysis for Averages of LRP attributions Taal: Test-time augmentation for active learning in medical image segmentation,

Reference 42

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Value bounds and Convergence Analysis for Averages of LRP attributions Improving generalization capability of deep learning-based nuclei instance segmentation by non-deterministic train time and deterministic test time stain normalization,

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Value bounds and Convergence Analysis for Averages of LRP attributions Multi-modality microscopy image style augmentation for nuclei segmentation,

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Value bounds and Convergence Analysis for Averages of LRP attributions Stain-robust mitotic figure detection for the mitosis domain generalization challenge,

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Value bounds and Convergence Analysis for Averages of LRP attributions Test-time generative augmentation for medical image segmentation,

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Value bounds and Convergence Analysis for Averages of LRP attributions Betteraggregationintest-timeaugmentation,

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Value bounds and Convergence Analysis for Averages of LRP attributions Test-time data augmentation for estimation of heteroscedastic aleatoric uncertainty in deep neural networks,

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Value bounds and Convergence Analysis for Averages of LRP attributions Layer-wise relevance propagation: an overview,

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Value bounds and Convergence Analysis for Averages of LRP attributions Das asymptotische Verteilungsgesetz der Eigenwerte linearer partieller Differentialgleichungen (mit einer Anwendung auf die Theorie der Hohlraumstrahlung),

Reference 50

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Value bounds and Convergence Analysis for Averages of LRP attributions Unresolved cited work

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Value bounds and Convergence Analysis for Averages of LRP attributions Probability inequalities for sums of bounded random variables,

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Value bounds and Convergence Analysis for Averages of LRP attributions Deep residual learning for image recognition,

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Value bounds and Convergence Analysis for Averages of LRP attributions EfficientNetV2: Smaller models and faster training,

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Value bounds and Convergence Analysis for Averages of LRP attributions Pytorch: An imperative style, high-performance deep learning library,

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Value bounds and Convergence Analysis for Averages of LRP attributions Swin transformer V2: scaling up capacity and resolution,

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Value bounds and Convergence Analysis for Averages of LRP attributions Imagenet: A large-scale hierarchical image database,

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Value bounds and Convergence Analysis for Averages of LRP attributions We can assume without loss of generality after ordering the terms according to the sign ofwks ·z s that M[:, k] = ((1 +β)p1,+,

Reference 58

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