layer-wise relevance propagation

Hidden Monotonicity: Explaining Deep Neural Networks via their DC Decomposition CVPR 2026 On Explaining Equivariant Graph Networks via Improved Relevance Propagation ICML 2025 When LRP Diverges from Leave-One-Out in Transformers EMNLP 2025 AttnLRP: Attention-Aware Layer-Wise Relevance Propagation for Transformers ICML 2024 Better Explain Transformers by Illuminating Important Information EACL 2024 Empowering CAM-Based Methods with Capability to Generate Fine-Grained and High-Faithfulness Explanations AAAI 2024 Explaining Text Similarity in Transformer Models NAACL 2024 MambaLRP: Explaining Selective State Space Sequence Models NeurIPS 2024 xMIL: Insightful Explanations for Multiple Instance Learning in Histopathology NeurIPS 2024 Relevant Walk Search for Explaining Graph Neural Networks ICML 2023 Efficient Computation of Higher-Order Subgraph Attribution via Message Passing ICML 2022 Rethinking Attention-Model Explainability through Faithfulness Violation Test ICML 2022 Analyzing the Source and Target Contributions to Predictions in Neural Machine Translation ACL 2021 DeepPseudo: Pseudo Value Based Deep Learning Models for Competing Risk Analysis AAAI 2021 Relevance-CAM: Your Model Already Knows Where To Look CVPR 2021 Exploring Contextual Word-level Style Relevance for Unsupervised Style Transfer ACL 2020 Relative Attributing Propagation: Interpreting the Comparative Contributions of Individual Units in Deep Neural Networks AAAI 2020 When Explanations Lie: Why Many Modified BP Attributions Fail ICML 2020 Auditing Deep Learning processes through Kernel-based Explanatory Models EMNLP 2019 Explaining non-linear Classifier Decisions within Kernel-based Deep Architectures EMNLP 2018 Refining Pretrained Word Embeddings Using Layer-wise Relevance Propagation EMNLP 2018 Visualizing and Understanding Neural Machine Translation ACL 2017 Analyzing Classifiers: Fisher Vectors and Deep Neural Networks CVPR 2016 The LRP Toolbox for Artificial Neural Networks JMLR 2016