Layer-wise Relevance Propagation for Neural Networks with Local Renormalization Layers

4 April 2016

Papers citing "Layer-wise Relevance Propagation for Neural Networks with Local Renormalization Layers"

34 / 84 papers shown

Title
IA-RED $^2$ : Interpretability-Aware Redundancy Reduction for Vision Transformers Bowen Pan Yikang Shen Yi Ding Zhangyang Wang Rogerio Feris A. Oliva VLM ViT 39 153 0 23 Jun 2021
FairCanary: Rapid Continuous Explainable Fairness Avijit Ghosh Aalok Shanbhag Christo Wilson 11 20 0 13 Jun 2021
Causal Abstractions of Neural Networks Atticus Geiger Hanson Lu Thomas Icard Christopher Potts NAI CML 17 222 0 06 Jun 2021
Explainable Machine Learning with Prior Knowledge: An Overview Katharina Beckh Sebastian Müller Matthias Jakobs Vanessa Toborek Hanxiao Tan Raphael Fischer Pascal Welke Sebastian Houben Laura von Rueden XAI 22 28 0 21 May 2021
Zorro: Valid, Sparse, and Stable Explanations in Graph Neural Networks Thorben Funke Megha Khosla Mandeep Rathee Avishek Anand FAtt 23 38 0 18 May 2021
Bias, Fairness, and Accountability with AI and ML Algorithms Neng-Zhi Zhou Zach Zhang V. Nair Harsh Singhal Jie Chen Agus Sudjianto FaML 21 9 0 13 May 2021
Knowledge Neurons in Pretrained Transformers Damai Dai Li Dong Y. Hao Zhifang Sui Baobao Chang Furu Wei KELM MU 28 418 0 18 Apr 2021
Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Survey Thomas Rojat Raphael Puget David Filliat Javier Del Ser R. Gelin Natalia Díaz Rodríguez XAI AI4TS 44 128 0 02 Apr 2021
Robust Models Are More Interpretable Because Attributions Look Normal Zifan Wang Matt Fredrikson Anupam Datta OOD FAtt 35 25 0 20 Mar 2021
Transformer Interpretability Beyond Attention Visualization Hila Chefer Shir Gur Lior Wolf 45 644 0 17 Dec 2020
Shapley Flow: A Graph-based Approach to Interpreting Model Predictions Jiaxuan Wang Jenna Wiens Scott M. Lundberg FAtt 25 88 0 27 Oct 2020
Learning Propagation Rules for Attribution Map Generation Yiding Yang Jiayan Qiu Xiuming Zhang Dacheng Tao Xinchao Wang FAtt 38 17 0 14 Oct 2020
SHAP values for Explaining CNN-based Text Classification Models Wei Zhao Tarun Joshi V. Nair Agus Sudjianto FAtt 28 36 0 26 Aug 2020
Sequential Explanations with Mental Model-Based Policies A. Yeung Shalmali Joshi Joseph Jay Williams Frank Rudzicz FAtt LRM 31 15 0 17 Jul 2020
The Penalty Imposed by Ablated Data Augmentation Frederick Liu A. Najmi Mukund Sundararajan 31 6 0 08 Jun 2020
Attribution in Scale and Space Shawn Xu Subhashini Venugopalan Mukund Sundararajan FAtt BDL 14 71 0 03 Apr 2020
Self-Supervised Discovering of Interpretable Features for Reinforcement Learning Wenjie Shi Gao Huang Shiji Song Zhuoyuan Wang Tingyu Lin Cheng Wu SSL 28 18 0 16 Mar 2020
Neuron Shapley: Discovering the Responsible Neurons Amirata Ghorbani James Zou FAtt TDI 25 108 0 23 Feb 2020
Explaining Explanations: Axiomatic Feature Interactions for Deep Networks Joseph D. Janizek Pascal Sturmfels Su-In Lee FAtt 30 143 0 10 Feb 2020
Q-value Path Decomposition for Deep Multiagent Reinforcement Learning Yaodong Yang Jianye Hao Guangyong Chen Hongyao Tang Yingfeng Chen Yujing Hu Changjie Fan Zhongyu Wei 23 52 0 10 Feb 2020
Feature relevance quantification in explainable AI: A causal problem Dominik Janzing Lenon Minorics Patrick Blobaum FAtt CML 15 278 0 29 Oct 2019
Interpreting Undesirable Pixels for Image Classification on Black-Box Models Sin-Han Kang Hong G Jung Seong-Whan Lee FAtt 19 3 0 27 Sep 2019
Improving performance of deep learning models with axiomatic attribution priors and expected gradients G. Erion Joseph D. Janizek Pascal Sturmfels Scott M. Lundberg Su-In Lee OOD BDL FAtt 21 80 0 25 Jun 2019
Software and application patterns for explanation methods Maximilian Alber 38 11 0 09 Apr 2019
Unmasking Clever Hans Predictors and Assessing What Machines Really Learn Sebastian Lapuschkin S. Wäldchen Alexander Binder G. Montavon Wojciech Samek K. Müller 17 996 0 26 Feb 2019
DeepPINK: reproducible feature selection in deep neural networks Yang Young Lu Yingying Fan Jinchi Lv William Stafford Noble FAtt 27 124 0 04 Sep 2018
Shedding Light on Black Box Machine Learning Algorithms: Development of an Axiomatic Framework to Assess the Quality of Methods that Explain Individual Predictions Milo Honegger 19 35 0 15 Aug 2018
A Note about: Local Explanation Methods for Deep Neural Networks lack Sensitivity to Parameter Values Mukund Sundararajan Ankur Taly FAtt 19 21 0 11 Jun 2018
How Important Is a Neuron? Kedar Dhamdhere Mukund Sundararajan Qiqi Yan FAtt GNN 22 128 0 30 May 2018
Did the Model Understand the Question? Pramod Kaushik Mudrakarta Ankur Taly Mukund Sundararajan Kedar Dhamdhere ELM OOD FAtt 27 196 0 14 May 2018
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 234 2,238 0 24 Jun 2017
Axiomatic Attribution for Deep Networks Mukund Sundararajan Ankur Taly Qiqi Yan OOD FAtt 42 5,865 0 04 Mar 2017
Understanding intermediate layers using linear classifier probes Guillaume Alain Yoshua Bengio FAtt 53 897 0 05 Oct 2016
Identifying individual facial expressions by deconstructing a neural network F. Arbabzadah G. Montavon K. Müller Wojciech Samek CVBM FAtt 30 31 0 23 Jun 2016