Towards better understanding of gradient-based attribution methods for Deep Neural Networks

16 November 2017

Markus Gross

Papers citing "Towards better understanding of gradient-based attribution methods for Deep Neural Networks"

30 / 30 papers shown

Title
Building Bridges, Not Walls -- Advancing Interpretability by Unifying Feature, Data, and Model Component Attribution Shichang Zhang Tessa Han Usha Bhalla Hima Lakkaraju FAtt 150 0 0 17 Feb 2025
The Conformer Encoder May Reverse the Time Dimension Robin Schmitt Albert Zeyer Mohammad Zeineldeen Ralf Schluter Hermann Ney 36 0 0 01 Oct 2024
Model Interpretation and Explainability: Towards Creating Transparency in Prediction Models D. Kridel Jacob Dineen Daniel R. Dolk David G. Castillo 23 4 0 31 May 2024
What Sketch Explainability Really Means for Downstream Tasks Hmrishav Bandyopadhyay Pinaki Nath Chowdhury A. Bhunia Aneeshan Sain Tao Xiang Yi-Zhe Song 30 4 0 14 Mar 2024
Towards Interpretable Classification of Leukocytes based on Deep Learning S. Röhrl Johannes Groll M. Lengl Simon Schumann C. Klenk D. Heim Martin Knopp Oliver Hayden Klaus Diepold 30 2 0 24 Nov 2023
XAI-CLASS: Explanation-Enhanced Text Classification with Extremely Weak Supervision Daniel Hajialigol Hanwen Liu Xuan Wang VLM 21 5 0 31 Oct 2023
MAEA: Multimodal Attribution for Embodied AI Vidhi Jain Jayant Sravan Tamarapalli Sahiti Yerramilli Yonatan Bisk 39 0 0 25 Jul 2023
Lidar Line Selection with Spatially-Aware Shapley Value for Cost-Efficient Depth Completion Kamil Adamczewski Daniel Gehrig Vaishakh Patil Luc Van Gool 33 1 0 21 Mar 2023
Disentangled Explanations of Neural Network Predictions by Finding Relevant Subspaces Pattarawat Chormai J. Herrmann Klaus-Robert Muller G. Montavon FAtt 48 17 0 30 Dec 2022
Evaluating Feature Attribution Methods for Electrocardiogram J. Suh Jimyeong Kim Euna Jung Wonjong Rhee FAtt 17 2 0 23 Nov 2022
Human-Centered Concept Explanations for Neural Networks Chih-Kuan Yeh Been Kim Pradeep Ravikumar FAtt 37 25 0 25 Feb 2022
First is Better Than Last for Language Data Influence Chih-Kuan Yeh Ankur Taly Mukund Sundararajan Frederick Liu Pradeep Ravikumar TDI 32 20 0 24 Feb 2022
Morphological feature visualization of Alzheimer's disease via Multidirectional Perception GAN Wen Yu Baiying Lei Yanyan Shen Shuqiang Wang Yong Liu Z. Feng Yong Hu Michael K. Ng GAN MedIm 8 82 0 25 Nov 2021
Explainable Adversarial Attacks in Deep Neural Networks Using Activation Profiles G. Cantareira R. Mello F. Paulovich AAML 24 9 0 18 Mar 2021
Contrastive Graph Neural Network Explanation Lukas Faber A. K. Moghaddam Roger Wattenhofer 29 36 0 26 Oct 2020
Adversarial Infidelity Learning for Model Interpretation Jian Liang Bing Bai Yuren Cao Kun Bai Fei-Yue Wang AAML 46 18 0 09 Jun 2020
SCOUT: Self-aware Discriminant Counterfactual Explanations Pei Wang Nuno Vasconcelos FAtt 27 81 0 16 Apr 2020
Measuring and improving the quality of visual explanations Agnieszka Grabska-Barwiñska XAI FAtt 18 3 0 14 Mar 2020
When Explanations Lie: Why Many Modified BP Attributions Fail Leon Sixt Maximilian Granz Tim Landgraf BDL FAtt XAI 13 132 0 20 Dec 2019
Input-Cell Attention Reduces Vanishing Saliency of Recurrent Neural Networks Aya Abdelsalam Ismail Mohamed K. Gunady L. Pessoa H. C. Bravo S. Feizi AI4TS 25 50 0 27 Oct 2019
Evaluating Explanation Without Ground Truth in Interpretable Machine Learning Fan Yang Mengnan Du Xia Hu XAI ELM 27 66 0 16 Jul 2019
An Empirical Study towards Understanding How Deep Convolutional Nets Recognize Falls Yan Zhang Heiko Neumann 23 5 0 05 Dec 2018
Local Explanation Methods for Deep Neural Networks Lack Sensitivity to Parameter Values Julius Adebayo Justin Gilmer Ian Goodfellow Been Kim FAtt AAML 19 128 0 08 Oct 2018
Sanity Checks for Saliency Maps Julius Adebayo Justin Gilmer M. Muelly Ian Goodfellow Moritz Hardt Been Kim FAtt AAML XAI 61 1,927 0 08 Oct 2018
Extractive Adversarial Networks: High-Recall Explanations for Identifying Personal Attacks in Social Media Posts Samuel Carton Qiaozhu Mei Paul Resnick FAtt AAML 19 34 0 01 Sep 2018
Mitigating Sybils in Federated Learning Poisoning Clement Fung Chris J. M. Yoon Ivan Beschastnikh AAML 15 497 0 14 Aug 2018
Explaining Explanations: An Overview of Interpretability of Machine Learning Leilani H. Gilpin David Bau Ben Z. Yuan Ayesha Bajwa Michael A. Specter Lalana Kagal XAI 40 1,840 0 31 May 2018
Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges Gabrielle Ras Marcel van Gerven W. Haselager XAI 17 217 0 20 Mar 2018
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 234 2,238 0 24 Jun 2017
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation Yonghui Wu M. Schuster Z. Chen Quoc V. Le Mohammad Norouzi ... Alex Rudnick Oriol Vinyals G. Corrado Macduff Hughes J. Dean AIMat 716 6,746 0 26 Sep 2016