Learning how to explain neural networks: PatternNet and PatternAttribution

16 May 2017

Pieter-Jan Kindermans

Papers citing "Learning how to explain neural networks: PatternNet and PatternAttribution"

50 / 73 papers shown

Title
Explainable AI needs formal notions of explanation correctness Stefan Haufe Rick Wilming Benedict Clark Rustam Zhumagambetov Danny Panknin Ahcène Boubekki XAI 31 1 0 22 Sep 2024
Implementing local-explainability in Gradient Boosting Trees: Feature Contribution Ángel Delgado-Panadero Beatriz Hernández-Lorca María Teresa García-Ordás J. Benítez-Andrades 40 52 0 14 Feb 2024
On the Relationship Between Interpretability and Explainability in Machine Learning Benjamin Leblanc Pascal Germain FaML 29 0 0 20 Nov 2023
PAMI: partition input and aggregate outputs for model interpretation Wei Shi Wentao Zhang Weishi Zheng Ruixuan Wang FAtt 26 3 0 07 Feb 2023
AUTOLYCUS: Exploiting Explainable AI (XAI) for Model Extraction Attacks against Interpretable Models Abdullah Çaglar Öksüz Anisa Halimi Erman Ayday ELM AAML 21 2 0 04 Feb 2023
Explainable AI for Bioinformatics: Methods, Tools, and Applications Md. Rezaul Karim Tanhim Islam Oya Beyan Christoph Lange Michael Cochez Dietrich-Rebholz Schuhmann Stefan Decker 29 68 0 25 Dec 2022
Data-Adaptive Discriminative Feature Localization with Statistically Guaranteed Interpretation Ben Dai Xiaotong Shen Lingzhi Chen Chunlin Li Wei Pan FAtt 21 1 0 18 Nov 2022
Explainable Deep Learning to Profile Mitochondrial Disease Using High Dimensional Protein Expression Data Atif Khan C. Lawless Amy Vincent Satish Pilla S. Ramesh A. Mcgough 36 0 0 31 Oct 2022
From Attribution Maps to Human-Understandable Explanations through Concept Relevance Propagation Reduan Achtibat Maximilian Dreyer Ilona Eisenbraun S. Bosse Thomas Wiegand Wojciech Samek Sebastian Lapuschkin FAtt 30 131 0 07 Jun 2022
Visualizing Deep Neural Networks with Topographic Activation Maps A. Krug Raihan Kabir Ratul Christopher Olson Sebastian Stober FAtt AI4CE 36 3 0 07 Apr 2022
Investigating the fidelity of explainable artificial intelligence methods for applications of convolutional neural networks in geoscience Antonios Mamalakis E. Barnes I. Ebert‐Uphoff 26 73 0 07 Feb 2022
Navigating Neural Space: Revisiting Concept Activation Vectors to Overcome Directional Divergence Frederik Pahde Maximilian Dreyer Leander Weber Moritz Weckbecker Christopher J. Anders Thomas Wiegand Wojciech Samek Sebastian Lapuschkin 60 7 0 07 Feb 2022
Deeply Explain CNN via Hierarchical Decomposition Mingg-Ming Cheng Peng-Tao Jiang Linghao Han Liang Wang Philip H. S. Torr FAtt 53 15 0 23 Jan 2022
PCACE: A Statistical Approach to Ranking Neurons for CNN Interpretability Sílvia Casacuberta Esra Suel Seth Flaxman FAtt 21 1 0 31 Dec 2021
Evaluating saliency methods on artificial data with different background types Céline Budding Fabian Eitel K. Ritter Stefan Haufe XAI FAtt MedIm 27 5 0 09 Dec 2021
Improving Deep Learning Interpretability by Saliency Guided Training Aya Abdelsalam Ismail H. C. Bravo S. Feizi FAtt 20 80 0 29 Nov 2021
Evaluation of Interpretability for Deep Learning algorithms in EEG Emotion Recognition: A case study in Autism J. M. M. Torres Sara E. Medina-DeVilliers T. Clarkson M. Lerner Giuseppe Riccardi 30 34 0 25 Nov 2021
Visualizing the Emergence of Intermediate Visual Patterns in DNNs Mingjie Li Shaobo Wang Quanshi Zhang 27 11 0 05 Nov 2021
Transparency of Deep Neural Networks for Medical Image Analysis: A Review of Interpretability Methods Zohaib Salahuddin Henry C. Woodruff A. Chatterjee Philippe Lambin 18 302 0 01 Nov 2021
Interpreting Deep Learning Models in Natural Language Processing: A Review Xiaofei Sun Diyi Yang Xiaoya Li Tianwei Zhang Yuxian Meng Han Qiu Guoyin Wang Eduard H. Hovy Jiwei Li 17 44 0 20 Oct 2021
Discriminative Attribution from Counterfactuals N. Eckstein A. S. Bates G. Jefferis Jan Funke FAtt CML 27 1 0 28 Sep 2021
SoK: Machine Learning Governance Varun Chandrasekaran Hengrui Jia Anvith Thudi Adelin Travers Mohammad Yaghini Nicolas Papernot 38 16 0 20 Sep 2021
This looks more like that: Enhancing Self-Explaining Models by Prototypical Relevance Propagation Srishti Gautam Marina M.-C. Höhne Stine Hansen Robert Jenssen Michael C. Kampffmeyer 27 49 0 27 Aug 2021
Understanding of Kernels in CNN Models by Suppressing Irrelevant Visual Features in Images Jiafan Zhuang Wanying Tao Jianfei Xing Wei Shi Ruixuan Wang Weishi Zheng FAtt 37 3 0 25 Aug 2021
Explaining Bayesian Neural Networks Kirill Bykov Marina M.-C. Höhne Adelaida Creosteanu Klaus-Robert Muller Frederick Klauschen Shinichi Nakajima Marius Kloft BDL AAML 34 25 0 23 Aug 2021
Towards Interpretable Deep Networks for Monocular Depth Estimation Zunzhi You Yi-Hsuan Tsai W. Chiu Guanbin Li FAtt 34 17 0 11 Aug 2021
Improved Feature Importance Computations for Tree Models: Shapley vs. Banzhaf Adam Karczmarz A. Mukherjee Piotr Sankowski Piotr Wygocki FAtt TDI 14 6 0 09 Aug 2021
Software for Dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAy Christopher J. Anders David Neumann Wojciech Samek K. Müller Sebastian Lapuschkin 29 64 0 24 Jun 2021
A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Concepts Gesina Schwalbe Bettina Finzel XAI 29 184 0 15 May 2021
EXplainable Neural-Symbolic Learning (X-NeSyL) methodology to fuse deep learning representations with expert knowledge graphs: the MonuMAI cultural heritage use case Natalia Díaz Rodríguez Alberto Lamas Jules Sanchez Gianni Franchi Ivan Donadello S. Tabik David Filliat P. Cruz Rosana Montes Francisco Herrera 49 77 0 24 Apr 2021
Explainable Adversarial Attacks in Deep Neural Networks Using Activation Profiles G. Cantareira R. Mello F. Paulovich AAML 24 9 0 18 Mar 2021
Neural Network Attribution Methods for Problems in Geoscience: A Novel Synthetic Benchmark Dataset Antonios Mamalakis I. Ebert‐Uphoff E. Barnes OOD 28 75 0 18 Mar 2021
Towards Robust Explanations for Deep Neural Networks Ann-Kathrin Dombrowski Christopher J. Anders K. Müller Pan Kessel FAtt 21 63 0 18 Dec 2020
Self-Explaining Structures Improve NLP Models Zijun Sun Chun Fan Qinghong Han Xiaofei Sun Yuxian Meng Fei Wu Jiwei Li MILM XAI LRM FAtt 39 38 0 03 Dec 2020
Explaining by Removing: A Unified Framework for Model Explanation Ian Covert Scott M. Lundberg Su-In Lee FAtt 36 241 0 21 Nov 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
Trustworthy Convolutional Neural Networks: A Gradient Penalized-based Approach Nicholas F Halliwell Freddy Lecue FAtt 22 9 0 29 Sep 2020
A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research Cody Watson Nathan Cooper David Nader-Palacio Kevin Moran Denys Poshyvanyk 26 111 0 14 Sep 2020
Survey of XAI in digital pathology Milda Pocevičiūtė Gabriel Eilertsen Claes Lundström 11 56 0 14 Aug 2020
Explainable Face Recognition Jonathan R. Williford Brandon B. May J. Byrne CVBM 16 71 0 03 Aug 2020
Weakly-Supervised Cell Tracking via Backward-and-Forward Propagation Kazuya Nishimura Junya Hayashida Chenyang Wang Dai Fei Elmer Ker Ryoma Bise 26 17 0 30 Jul 2020
Interpreting and Disentangling Feature Components of Various Complexity from DNNs Jie Ren Mingjie Li Zexu Liu Quanshi Zhang CoGe 13 18 0 29 Jun 2020
Embedded Encoder-Decoder in Convolutional Networks Towards Explainable AI A. Tavanaei XAI 12 31 0 19 Jun 2020
Human-Expert-Level Brain Tumor Detection Using Deep Learning with Data Distillation and Augmentation D. Lu N. Polomac Iskra Gacheva E. Hattingen Jochen Triesch 18 18 0 17 Jun 2020
How Much Can I Trust You? -- Quantifying Uncertainties in Explaining Neural Networks Kirill Bykov Marina M.-C. Höhne Klaus-Robert Muller Shinichi Nakajima Marius Kloft UQCV FAtt 27 31 0 16 Jun 2020
Explainable deep learning models in medical image analysis Amitojdeep Singh S. Sengupta Vasudevan Lakshminarayanan XAI 35 482 0 28 May 2020
Explainable Deep Learning: A Field Guide for the Uninitiated Gabrielle Ras Ning Xie Marcel van Gerven Derek Doran AAML XAI 38 370 0 30 Apr 2020
A Survey of Deep Learning for Scientific Discovery M. Raghu Erica Schmidt OOD AI4CE 38 120 0 26 Mar 2020
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications Wojciech Samek G. Montavon Sebastian Lapuschkin Christopher J. Anders K. Müller XAI 44 82 0 17 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