Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability | Paper Citation Record

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This paper cites Quantifying attention flow in transformers.

Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability Quantifying attention flow in transformers

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This paper cites Attnlrp: attention-aware layer-wise relevance propagation for transformers.

Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability Attnlrp: attention-aware layer-wise relevance propagation for transformers

Reference 2

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Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability Xai for transformers: Better explanations through conservative propagation

Reference 3

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Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability Video and text matching with conditioned embeddings

Reference 4

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This paper cites The Hidden Attention of Mamba Models.

Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability The Hidden Attention of Mamba Models

Reference 5

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This paper cites Explaining recurrent neural network predictions in sentiment analysis.

Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability Explaining recurrent neural network predictions in sentiment analysis

Reference 6

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This paper cites Explaining and interpreting lstms.

Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability Explaining and interpreting lstms

Reference 7

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This paper cites Explainable artificial intelligence (xai): Concepts, taxonomies, opportunities and challenges toward responsible ai.

Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability Explainable artificial intelligence (xai): Concepts, taxonomies, opportunities and challenges toward responsible ai

Reference 8

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This paper cites On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation.

Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation

Reference 9

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Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability How to explain individual classification decisions

Reference 10

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Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability Qwen Technical Report

Reference 11

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This paper cites The shattered gradients problem: If resnets are the answer, then what is the question? In International conference on machine learning , pages 342–350.

Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability The shattered gradients problem: If resnets are the answer, then what is the question? In International conference on machine learning , pages 342–350

Reference 12

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This paper cites Pythia: A suite for analyzing large language models across training and scaling.

Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability Pythia: A suite for analyzing large language models across training and scaling

Reference 13

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This paper cites Emerging properties in self-supervised vision transformers.

Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability Emerging properties in self-supervised vision transformers

Reference 14

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

Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability Generic attention-model explainability for interpreting bi-modal and encoder-decoder transformers

Reference 15

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This paper cites Transformer interpretability beyond attention visualization.

Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability Transformer interpretability beyond attention visualization

Reference 16

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This paper cites Unifying Prediction and Explanation in Time-Series Transformers via Shapley-based Pretraining.

Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability Unifying Prediction and Explanation in Time-Series Transformers via Shapley-based Pretraining

Reference 17

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This paper cites What does bert look at? an analysis of bert’s attention.

Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability What does bert look at? an analysis of bert’s attention

Reference 18

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This paper cites Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey.

Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey

Reference 19

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This paper cites Position information in transformers: An overview.

Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability Position information in transformers: An overview

Reference 20

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Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability The Llama 3 Herd of Models

Reference 21

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Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability Imagenet auto-annotation with segmentation propagation

Reference 22

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Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability Long short-term memory

Reference 23

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This paper cites Explainable convolutional neural networks: a taxonomy, review, and future directions.

Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability Explainable convolutional neural networks: a taxonomy, review, and future directions

Reference 24

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Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability Mambalrp: Explaining selective state space sequence models

Reference 25

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Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability Attention is not explanation

Reference 26

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This paper cites Serial order: A parallel distributed processing approach.

Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability Serial order: A parallel distributed processing approach

Reference 27

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Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability Bert meets shapley: Extending shap explanations to transformer-based classifiers

Reference 28

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Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability Explaining in style: training a gan to explain a classifier in stylespace

Reference 29

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Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability Explainable ai: A review of machine learning interpretability methods

Reference 30

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Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability Explaining nonlinear classification decisions with deep taylor decomposition

Reference 31

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Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability Layer-wise relevance propagation: an overview

Reference 32

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Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability Shap- based explanation methods: a review for nlp interpretability

Reference 33

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Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability Train short, test long: Attention with linear biases enables input length extrapolation

Reference 34

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Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 35

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Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability SAM 2: Segment Anything in Images and Videos

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Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models

Reference 37

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Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability Grad-cam: Visual explanations from deep networks via gradient-based localization

Reference 38

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Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability Self-Attention with Relative Position Representations

Reference 39

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Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability Learning important features through propagating activation differences

Reference 40

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Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability Roformer: Enhanced transformer with rotary position embedding

Reference 41

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Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability Gemma: Open Models Based on Gemini Research and Technology

Reference 42

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Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability Training data-efficient image transformers & distillation through attention

Reference 43

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Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability LLaMA: Open and Efficient Foundation Language Models

Reference 44

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Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability Attention is all you need

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Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability Analyzing the source and target contributions to predictions in neural machine translation

Reference 46

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This paper cites Towards Explainable Artificial Intelligence (XAI): A Data Mining Perspective.

Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability Towards Explainable Artificial Intelligence (XAI): A Data Mining Perspective

Reference 47

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Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability Explaining information flow inside vision transformers using markov chain

Reference 48

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Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability TinyLlama: An Open-Source Small Language Model

Reference 49

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Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability Visual interpretability for deep learning: a survey

Reference 50

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This paper cites A survey on neural network interpretability.

Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability A survey on neural network interpretability

Reference 51

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This paper cites They should have been giving a tribute to Branagh for bringing us one of the greatest films of all time.

Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability They should have been giving a tribute to Branagh for bringing us one of the greatest films of all time

Reference 52

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