From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection | Paper Citation Record

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This paper cites write newline.

From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection write newline

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

From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection Slic superpixels compared to state-of-the-art superpixel methods

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From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection Sanity checks for saliency maps

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From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection B-cosification: Transforming deep neural networks to be inherently interpretable

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From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection and Shen, S

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From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection Meditationes sacrae

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This paper cites Discriminative feature attributions: bridging post hoc explainability and inherent interpretability.

From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection Discriminative feature attributions: bridging post hoc explainability and inherent interpretability

Reference 7

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This paper cites Recognition-by-components: a theory of human image understanding.

From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection Recognition-by-components: a theory of human image understanding

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From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection B-cos networks: Alignment is all we need for interpretability

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This paper cites B-cos alignment for inherently interpretable cnns and vision transformers.

From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection B-cos alignment for inherently interpretable cnns and vision transformers

Reference 10

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This paper cites Exploiting Interpretable Capabilities with Concept-Enhanced Diffusion and Prototype Networks.

From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection Exploiting Interpretable Capabilities with Concept-Enhanced Diffusion and Prototype Networks

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From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection Unresolved cited work

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

From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection Learning to explain: An information-theoretic perspective on model interpretation

Reference 13

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This paper cites J., Lee, S., Chun, S., Akata, Z., and Shim, H.

From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection J., Lee, S., Chun, S., Akata, Z., and Shim, H

Reference 14

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From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection C., Qiu, W., Lu, M., Kim, N

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This paper cites Towards A Rigorous Science of Interpretable Machine Learning.

From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection Towards A Rigorous Science of Interpretable Machine Learning

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From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection and Kim, B

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This paper cites Concept embedding models: Beyond the accuracy-explainability trade-off.

From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection Concept embedding models: Beyond the accuracy-explainability trade-off

Reference 18

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This paper cites Scaling rectified flow transformers for high-resolution image synthesis.

From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection Scaling rectified flow transformers for high-resolution image synthesis

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From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection Unresolved cited work

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

From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection Shapley values for feature selection: The good, the bad, and the axioms

Reference 21

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From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection Interpretations steered network pruning via amortized inferred saliency maps

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From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection Unresolved cited work

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From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection A benchmark for interpretability methods in deep neural networks

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From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection Searching for mobilenetv3

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From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection On the concept trustworthiness in concept bottleneck models

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From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection Towards Faithfully Interpretable NLP Systems: How should we define and evaluate faithfulness?

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From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection Categorical reparameterization with gumbel-softmax

Reference 28

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This paper cites Have we learned to explain?: How interpretability methods can learn to encode predictions in their interpretations.

From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection Have we learned to explain?: How interpretability methods can learn to encode predictions in their interpretations

Reference 29

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From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection Fast slic: Efficient superpixel segmentation, 2021

Reference 30

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From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection Adam: A Method for Stochastic Optimization

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From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection W., Nguyen, T., Tang, Y

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From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection Learning multiple layers of features from tiny images

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From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection N., Sun, J., Cetin, N., Al-Hazwani, I., Schlegel, U., Cheng, F., and El-Assady, M

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From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection Large Concept Models: Language Modeling in a Sentence Representation Space

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From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection The Mythos of Model Interpretability

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From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection The Doctor Just Won't Accept That!

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From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection Rotating features for object discovery

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From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection A Unified Approach to Interpreting Model Predictions

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From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection Interpretable image classification with adaptive prototype-based vision transformers

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From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection J., Mnih, A., and Teh, Y

Reference 43

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From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection and Vogt, J

Reference 44

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From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection Unresolved cited work

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From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection Stochastic segmentation networks: Modelling spatially correlated aleatoric uncertainty

Reference 47

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From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection and Protzel, P

Reference 48

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From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection Local Feature Selection without Label or Feature Leakage for Interpretable Machine Learning Predictions

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From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection Unresolved cited work

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From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection RISE: Randomized Input Sampling for Explanation of Black-box Models

Reference 51

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From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection and Bolon-Canedo, V

Reference 52

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From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection why should i trust you?

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From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection Metaphysics

Reference 54

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This paper cites Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead.

From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

Reference 55

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From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection Imagenet large scale visual recognition challenge

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This paper cites A review of feature selection techniques in bioinformatics.

From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection A review of feature selection techniques in bioinformatics

Reference 57

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From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D

Reference 58

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From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection Learning important features through propagating activation differences

Reference 59

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From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection Striving for Simplicity: The All Convolutional Net

Reference 60

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From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection Axiomatic attribution for deep networks

Reference 61

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From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection S., Mrabti, F., and Zahi, A

Reference 62

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From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection A., Havaei, M., Berthier, T., Dutil, F., Di Jorio, L., Hamarneh, G., and Bengio, Y

Reference 63

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From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection Regression shrinkage and selection via the lasso

Reference 64

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From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection Training data-efficient image transformers & distillation through attention

Reference 65

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From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection Unresolved cited work

Reference 66

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This paper cites Y., Engstrom, L., Ilyas, A., and Madry, A.

From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection Y., Engstrom, L., Ilyas, A., and Madry, A

Reference 67

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From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection Benchmarking Attribution Methods with Relative Feature Importance

Reference 68

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From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection Invase: Instance-wise variable selection using neural networks

Reference 69

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This paper cites and Lin, Y.

From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection and Lin, Y

Reference 70

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This paper cites Comprehensive attribution: Inherently explainable vision model with feature detector.

From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection Comprehensive attribution: Inherently explainable vision model with feature detector

Reference 71

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From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection A survey on evaluation methods for image segmentation

Reference 72

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From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection Places: A 10 million image database for scene recognition

Reference 73

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