Unmasking Clever Hans Predictors and Assessing What Machines Really Learn

Nature Communications (Nat Commun), 2019

26 February 2019

Papers citing "Unmasking Clever Hans Predictors and Assessing What Machines Really Learn"

50 / 382 papers shown

Title
Simplifying the explanation of deep neural networks with sufficient and necessary feature-sets: case of text classification Florentin Flambeau Jiechieu Kameni Norbert Tsopzé XAI FAtt MedIm 89 1 0 08 Oct 2020
Geometric Disentanglement by Random Convex Polytopes M. Joswig M. Kaluba Lukas Ruff 137 4 0 29 Sep 2020
Measure Utility, Gain Trust: Practical Advice for XAI Researcher B. Pierson M. Glenski William I. N. Sealy Dustin L. Arendt 127 29 0 27 Sep 2020
A Unifying Review of Deep and Shallow Anomaly DetectionProceedings of the IEEE (Proc. IEEE), 2020 Lukas Ruff Jacob R. Kauffmann Robert A. Vandermeulen G. Montavon Wojciech Samek Matthias Kirchler Thomas G. Dietterich Klaus-Robert Muller UQCV 488 920 0 24 Sep 2020
Survey of explainable machine learning with visual and granular methods beyond quasi-explanationsStudies in Computational Intelligence (SCI), 2020 Boris Kovalerchuk M. Ahmad University of Washington Tacoma 189 50 0 21 Sep 2020
Contextual Semantic InterpretabilityAsian Conference on Computer Vision (ACCV), 2020 Diego Marcos Ruth C. Fong Sylvain Lobry Rémi Flamary Nicolas Courty D. Tuia SSL 207 28 0 18 Sep 2020
Generalization on the Enhancement of Layerwise Relevance Interpretability of Deep Neural Network Erico Tjoa Cuntai Guan FAtt 155 0 0 05 Sep 2020
A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World LearningNeural Networks (NN), 2020 Martin Mundt Yongjun Hong Iuliia Pliushch Visvanathan Ramesh CLL 287 171 0 03 Sep 2020
Langevin Cooling for Domain Translation Vignesh Srinivasan Klaus-Robert Muller Wojciech Samek Shinichi Nakajima 140 1 0 31 Aug 2020
Survey of XAI in digital pathology Milda Pocevičiūtė Gabriel Eilertsen Claes Lundström 154 58 0 14 Aug 2020
Trustworthy AI Inference Systems: An Industry Research View Rosario Cammarota M. Schunter Anand Rajan Fabian Boemer Ágnes Kiss ... Aydin Aysu Fateme S. Hosseini Chengmo Yang Eric Wallace Pam Norton 198 17 0 10 Aug 2020
Revisiting the Modifiable Areal Unit Problem in Deep Traffic Prediction with Visual AnalyticsIEEE Transactions on Visualization and Computer Graphics (TVCG), 2020 Wei Zeng Chengqiao Lin Juncong Lin Jincheng Jiang Jiazhi Xia Cagatay Turkay Wei Chen 78 32 0 30 Jul 2020
Split and Expand: An inference-time improvement for Weakly Supervised Cell Instance Segmentation Lin Geng Foo Rui En Ho Jiamei Sun Alexander Binder 210 0 0 21 Jul 2020
Fairwashing Explanations with Off-Manifold Detergent Christopher J. Anders Plamen Pasliev Ann-Kathrin Dombrowski K. Müller Pan Kessel FAtt FaML 140 102 0 20 Jul 2020
Explanation-Guided Training for Cross-Domain Few-Shot ClassificationInternational Conference on Pattern Recognition (ICPR), 2020 Jiamei Sun Sebastian Lapuschkin Wojciech Samek Yunqing Zhao Ngai-Man Cheung Alexander Binder 227 107 0 17 Jul 2020
Explainable Deep Learning for Uncovering Actionable Scientific Insights for Materials Discovery and Design Shusen Liu B. Kailkhura Jize Zhang A. Hiszpanski Emily Robertson Donald Loveland T. Y. Han 102 2 0 16 Jul 2020
Explainable Deep One-Class Classification Philipp Liznerski Lukas Ruff Robert A. Vandermeulen Billy Joe Franks Matthias Kirchler Klaus-Robert Muller 382 229 0 03 Jul 2020
Drug discovery with explainable artificial intelligence José Jiménez-Luna F. Grisoni G. Schneider 289 713 0 01 Jul 2020
Actionable Attribution Maps for Scientific Machine Learning Shusen Liu B. Kailkhura Jize Zhang A. Hiszpanski Emily Robertson Donald Loveland T. Y. Han 79 1 0 30 Jun 2020
The Clever Hans Effect in Anomaly Detection Jacob R. Kauffmann Lukas Ruff G. Montavon Klaus-Robert Muller AAML 126 34 0 18 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 Matthias Kirchler UQCV FAtt 282 34 0 16 Jun 2020
Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey Arun Das P. Rad XAI 346 685 0 16 Jun 2020
Higher-Order Explanations of Graph Neural Networks via Relevant Walks Thomas Schnake Oliver Eberle Jonas Lederer Shinichi Nakajima Kristof T. Schütt Klaus-Robert Muller G. Montavon 283 273 0 05 Jun 2020
Black-box Explanation of Object Detectors via Saliency Maps Vitali Petsiuk R. Jain Varun Manjunatha Vlad I. Morariu Ashutosh Mehra Vicente Ordonez Kate Saenko FAtt 222 145 0 05 Jun 2020
Explainable Artificial Intelligence: a Systematic Review Giulia Vilone Luca Longo XAI 519 299 0 29 May 2020
Local and Global Explanations of Agent Behavior: Integrating Strategy Summaries with Saliency Maps Tobias Huber Katharina Weitz Elisabeth André Ofra Amir FAtt 264 69 0 18 May 2020
InteractionNet: Modeling and Explaining of Noncovalent Protein-Ligand Interactions with Noncovalent Graph Neural Network and Layer-Wise Relevance Propagation Hyeoncheol Cho E. Lee I. Choi GNN FAtt 117 6 0 12 May 2020
Evaluation, Tuning and Interpretation of Neural Networks for Meteorological ApplicationsBulletin of The American Meteorological Society - (BAMS) (BAMS), 2020 I. Ebert‐Uphoff Kyle Hilburn 194 32 0 06 May 2020
Towards explainable classifiers using the counterfactual approach -- global explanations for discovering bias in data Agnieszka Mikołajczyk M. Grochowski Arkadiusz Kwasigroch FAtt CML 91 3 0 05 May 2020
A neural network walks into a lab: towards using deep nets as models for human behavior Wei-Ying Ma B. Peters HAI AI4CE 154 59 0 02 May 2020
Attribution Analysis of Grammatical Dependencies in LSTMs Sophie Hao 201 3 0 30 Apr 2020
Development and Interpretation of a Neural Network-Based Synthetic Radar Reflectivity Estimator Using GOES-R Satellite ObservationsJournal of Applied Meteorology and Climatology (JAMC), 2020 Kyle Hilburn I. Ebert‐Uphoff S. Miller 128 58 0 16 Apr 2020
Shortcut Learning in Deep Neural NetworksNature Machine Intelligence (NMI), 2020 Robert Geirhos J. Jacobsen Claudio Michaelis R. Zemel Wieland Brendel Matthias Bethge Felix Wichmann 818 2,374 0 16 Apr 2020
Explainable Image Classification with Evidence Counterfactual T. Vermeire David Martens FAtt 73 0 0 16 Apr 2020
Overinterpretation reveals image classification model pathologiesNeural Information Processing Systems (NeurIPS), 2020 Brandon Carter Siddhartha Jain Jonas W. Mueller David K Gifford FAtt 211 55 0 19 Mar 2020
Vulnerabilities of Connectionist AI Applications: Evaluation and DefenceFrontiers in Big Data (Front. Big Data), 2020 Christian Berghoff Matthias Neu Arndt von Twickel AAML 165 26 0 18 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 335 86 0 17 Mar 2020
Ground Truth Evaluation of Neural Network Explanations with CLEVR-XAIInformation Fusion (Inf. Fusion), 2020 L. Arras Ahmed Osman Wojciech Samek XAI AAML 214 182 0 16 Mar 2020
Building and Interpreting Deep Similarity ModelsIEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020 Oliver Eberle Jochen Büttner Florian Kräutli K. Müller Matteo Valleriani G. Montavon 146 76 0 11 Mar 2020
Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance MethodologyMachine Learning and Knowledge Extraction (MLKE), 2020 Stefan Studer T. Bui C. Drescher A. Hanuschkin Ludwig Winkler S. Peters Klaus-Robert Muller 245 213 0 11 Mar 2020
The Pragmatic Turn in Explainable Artificial Intelligence (XAI)Minds and Machines (MM), 2019 Andrés Páez 165 214 0 22 Feb 2020
Forecasting Industrial Aging Processes with Machine Learning MethodsComputers and Chemical Engineering (CCE), 2020 Mihail Bogojeski Simeon Sauer F. Horn K. Müller AI4CE 141 22 0 05 Feb 2020
Evaluating Saliency Map Explanations for Convolutional Neural Networks: A User StudyInternational Conference on Intelligent User Interfaces (IUI), 2020 Ahmed Alqaraawi M. Schuessler Philipp Weiß Enrico Costanza N. Bianchi-Berthouze AAML FAtt XAI 189 208 0 03 Feb 2020
Big-Data Science in Porous Materials: Materials Genomics and Machine LearningChemical Reviews (Chem. Rev.), 2020 Kevin Maik Jablonka D. Ongari S. M. Moosavi B. Smit AI4CE 203 398 0 18 Jan 2020
Making deep neural networks right for the right scientific reasons by interacting with their explanationsNature Machine Intelligence (NMI), 2020 P. Schramowski Wolfgang Stammer Stefano Teso Anna Brugger Xiaoting Shao Hans-Georg Luigs Anne-Katrin Mahlein Kristian Kersting 472 238 0 15 Jan 2020
Explain and Improve: LRP-Inference Fine-Tuning for Image Captioning ModelsInformation Fusion (Inf. Fusion), 2020 Jiamei Sun Sebastian Lapuschkin Wojciech Samek Alexander Binder FAtt 482 36 0 04 Jan 2020
Finding and Removing Clever Hans: Using Explanation Methods to Debug and Improve Deep Models Christopher J. Anders Talmaj Marinc David Neumann Wojciech Samek K. Müller Sebastian Lapuschkin AAML 215 20 0 22 Dec 2019
Analysis of Video Feature Learning in Two-Stream CNNs on the Example of Zebrafish Swim Bout ClassificationInternational Conference on Learning Representations (ICLR), 2019 Bennet Breier A. Onken 73 4 0 20 Dec 2019
Pruning by Explaining: A Novel Criterion for Deep Neural Network PruningPattern Recognition (Pattern Recognit.), 2019 Seul-Ki Yeom P. Seegerer Sebastian Lapuschkin Alexander Binder Simon Wiedemann K. Müller Wojciech Samek CVBM 209 236 0 18 Dec 2019
Analysing Deep Reinforcement Learning Agents Trained with Domain Randomisation Tianhong Dai Kai Arulkumaran Tamara Gerbert Samyakh Tukra Feryal M. P. Behbahani Anil Anthony Bharath 186 30 0 18 Dec 2019