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

Title
Towards an Explanation Space to Align Humans and Explainable-AI Teamwork G. Cabour A. Morales É. Ledoux S. Bassetto 122 5 0 02 Jun 2021
Optimal Sampling Density for Nonparametric Regression Danny Panknin Klaus-Robert Muller Shinichi Nakajima 111 2 0 25 May 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 265 31 0 21 May 2021
Expressive Explanations of DNNs by Combining Concept Analysis with ILPDeutsche Jahrestagung für Künstliche Intelligenz (KI), 2020 Johannes Rabold Gesina Schwalbe Ute Schmid 94 23 0 16 May 2021
A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and ConceptsData mining and knowledge discovery (DMKD), 2021 Gesina Schwalbe Bettina Finzel XAI 349 258 0 15 May 2021
Evading the Simplicity Bias: Training a Diverse Set of Models Discovers Solutions with Superior OOD GeneralizationComputer Vision and Pattern Recognition (CVPR), 2021 Damien Teney Ehsan Abbasnejad Simon Lucey Anton Van Den Hengel 446 100 0 12 May 2021
Towards Benchmarking the Utility of Explanations for Model Debugging Maximilian Idahl Lijun Lyu U. Gadiraju Avishek Anand XAI 139 19 0 10 May 2021
This Looks Like That... Does it? Shortcomings of Latent Space Prototype Interpretability in Deep Networks Adrian Hoffmann Claudio Fanconi Rahul Rade Jonas Köhler 232 72 0 05 May 2021
SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal EffectsNature Communications (Nat Commun), 2021 Oliver T. Unke Stefan Chmiela M. Gastegger Kristof T. Schütt H. E. Sauceda K. Müller 429 309 0 01 May 2021
Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety Sebastian Houben Stephanie Abrecht Maram Akila Andreas Bär Felix Brockherde ... Serin Varghese Michael Weber Sebastian J. Wirkert Tim Wirtz Matthias Woehrle AAML 285 61 0 29 Apr 2021
Why AI is Harder Than We Think Melanie Mitchell 252 119 0 26 Apr 2021
Patch Shortcuts: Interpretable Proxy Models Efficiently Find Black-Box Vulnerabilities Julia Rosenzweig Joachim Sicking Sebastian Houben Michael Mock Maram Akila AAML 254 3 0 22 Apr 2021
Explainable artificial intelligence for mechanics: physics-informing neural networks for constitutive models A. Koeppe F. Bamer M. Selzer B. Nestler Bernd Markert PINN AI4CE 312 9 0 20 Apr 2021
NICE: An Algorithm for Nearest Instance Counterfactual ExplanationsData mining and knowledge discovery (DMKD), 2021 Dieter Brughmans Pieter Leyman David Martens 217 79 0 15 Apr 2021
A Conceptual Framework for Establishing Trust in Real World Intelligent SystemsCognitive Systems Research (CSR), 2021 Michael Guckert Nils Gumpfer J. Hannig Till Keller N. Urquhart 86 3 0 12 Apr 2021
Towards a Collective Agenda on AI for Earth Science Data AnalysisIEEE Geoscience and Remote Sensing Magazine (GRSM), 2021 D. Tuia R. Roscher Jan Dirk Wegner Nathan Jacobs Xiaoxiang Zhu Gustau Camps-Valls AI4CE 199 80 0 11 Apr 2021
White Box Methods for Explanations of Convolutional Neural Networks in Image Classification Tasks Meghna P. Ayyar J. Benois-Pineau A. Zemmari FAtt 139 23 0 06 Apr 2021
Evaluating explainable artificial intelligence methods for multi-label deep learning classification tasks in remote sensingInternational Journal of Applied Earth Observation and Geoinformation (JAEOG), 2021 Ioannis Kakogeorgiou Konstantinos Karantzalos XAI 160 140 0 03 Apr 2021
STARdom: an architecture for trusted and secure human-centered manufacturing systemsAdvances in Production Management Systems (APMS), 2021 Jože M. Rožanec Patrik Zajec K. Kenda I. Novalija B. Fortuna ... Diego Reforgiato Recupero D. Kyriazis G. Sofianidis Spyros Theodoropoulos John Soldatos 149 9 0 02 Apr 2021
Interpretable Machine Learning: Fundamental Principles and 10 Grand ChallengesStatistics Survey (Stat. Surv.), 2021 Cynthia Rudin Chaofan Chen Zhi Chen Haiyang Huang Lesia Semenova Chudi Zhong FaML AI4CE LRM 391 840 0 20 Mar 2021
Neural Network Attribution Methods for Problems in Geoscience: A Novel Synthetic Benchmark DatasetEnvironmental Data Science (EDS), 2021 Antonios Mamalakis I. Ebert‐Uphoff E. Barnes OOD 311 87 0 18 Mar 2021
Explanations in Autonomous Driving: A Survey Daniel Omeiza Helena Webb Marina Jirotka Lars Kunze 424 277 0 09 Mar 2021
Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and ApplicationsInformation Fusion (Inf. Fusion), 2021 Yu-Liang Chou Catarina Moreira P. Bruza Chun Ouyang Joaquim A. Jorge CML 319 214 0 07 Mar 2021
Deep Learning Based Decision Support for Medicine -- A Case Study on Skin Cancer Diagnosis Adriano Lucieri Andreas Dengel Sheraz Ahmed 185 8 0 02 Mar 2021
Uncertainty Quantification by Ensemble Learning for Computational Optical Form Measurements L. Hoffmann I. Fortmeier Clemens Elster UQCV 145 34 0 01 Mar 2021
KANDINSKYPatterns -- An experimental exploration environment for Pattern Analysis and Machine Intelligence Andreas Holzinger Anna Saranti Heimo Mueller 250 11 0 28 Feb 2021
PredDiff: Explanations and Interactions from Conditional ExpectationsArtificial Intelligence (AI), 2021 Stefan Blücher Johanna Vielhaben Nils Strodthoff FAtt 251 22 0 26 Feb 2021
Benchmarking and Survey of Explanation Methods for Black Box ModelsData mining and knowledge discovery (DMKD), 2021 F. Bodria F. Giannotti Riccardo Guidotti Francesca Naretto D. Pedreschi S. Rinzivillo XAI 318 278 0 25 Feb 2021
Bandits for Learning to Explain from Explanations Freya Behrens Stefano Teso Davide Mottin FAtt 102 1 0 07 Feb 2021
MC-LSTM: Mass-Conserving LSTMInternational Conference on Machine Learning (ICML), 2021 Pieter-Jan Hoedt Frederik Kratzert D. Klotz Christina Halmich Markus Holzleitner G. Nearing Sepp Hochreiter Günter Klambauer 199 67 0 13 Jan 2021
Quantitative Evaluations on Saliency Methods: An Experimental Study Xiao-hui Li Yuhan Shi Haoyang Li Wei Bai Yuanwei Song Caleb Chen Cao Lei Chen FAtt XAI 237 23 0 31 Dec 2020
A Survey on Neural Network InterpretabilityIEEE Transactions on Emerging Topics in Computational Intelligence (IEEE TETCI), 2020 Yu Zhang Peter Tiño A. Leonardis Shengcai Liu FaML XAI 462 814 0 28 Dec 2020
GANterfactual - Counterfactual Explanations for Medical Non-Experts using Generative Adversarial LearningFrontiers in Artificial Intelligence (FAI), 2020 Silvan Mertes Tobias Huber Katharina Weitz Alexander Heimerl Elisabeth André GAN AAML MedIm 288 102 0 22 Dec 2020
Towards Robust Explanations for Deep Neural NetworksPattern Recognition (Pattern Recognit.), 2020 Ann-Kathrin Dombrowski Christopher J. Anders K. Müller Pan Kessel FAtt 195 66 0 18 Dec 2020
Interpreting Deep Neural Networks with Relative Sectional Propagation by Analyzing Comparative Gradients and Hostile ActivationsAAAI Conference on Artificial Intelligence (AAAI), 2020 Woo-Jeoung Nam Jaesik Choi Seong-Whan Lee FAtt AAML 258 17 0 07 Dec 2020
Explaining Predictions of Deep Neural Classifier via Activation Analysis M. Stano Wanda Benesova L. Marták FAtt AAML HAI 118 4 0 03 Dec 2020
TimeSHAP: Explaining Recurrent Models through Sequence PerturbationsKnowledge Discovery and Data Mining (KDD), 2020 João Bento Pedro Saleiro André F. Cruz Mário A. T. Figueiredo P. Bizarro FAtt AI4TS 313 123 0 30 Nov 2020
Explaining Deep Learning Models for Structured Data using Layer-Wise Relevance Propagation hsan Ullah André Ríos Vaibhav Gala Susan Mckeever FAtt 178 10 0 26 Nov 2020
Advancing diagnostic performance and clinical usability of neural networks via adversarial training and dual batch normalizationNature Communications (Nat Commun), 2020 T. Han S. Nebelung F. Pedersoli Markus Zimmermann M. Schulze-Hagen ... Christoph Haarburger Fabian Kiessling Christiane Kuhl Volkmar Schulz Daniel Truhn MedIm 116 37 0 25 Nov 2020
Right for the Right Concept: Revising Neuro-Symbolic Concepts by Interacting with their ExplanationsComputer Vision and Pattern Recognition (CVPR), 2020 Wolfgang Stammer P. Schramowski Kristian Kersting FAtt 452 126 0 25 Nov 2020
Gradient Starvation: A Learning Proclivity in Neural NetworksNeural Information Processing Systems (NeurIPS), 2020 Mohammad Pezeshki Sekouba Kaba Yoshua Bengio Aaron Courville Doina Precup Guillaume Lajoie MLT 448 302 0 18 Nov 2020
Use HiResCAM instead of Grad-CAM for faithful explanations of convolutional neural networks R. Draelos Lawrence Carin FAtt 282 137 0 17 Nov 2020
Debiasing Convolutional Neural Networks via Meta Orthogonalization Kurtis Evan David Qiang Liu Ruth C. Fong FaML 85 3 0 15 Nov 2020
Debugging Tests for Model Explanations Julius Adebayo M. Muelly Ilaria Liccardi Been Kim FAtt 266 198 0 10 Nov 2020
Explain by Evidence: An Explainable Memory-based Neural Network for Question Answering Quan Hung Tran Nhan Dam T. Lai Franck Dernoncourt Trung Le Nham Le Dinh Q. Phung FAtt 97 4 0 05 Nov 2020
MAPS-X: Explainable Multi-Robot Motion Planning via SegmentationIEEE International Conference on Robotics and Automation (ICRA), 2020 Justin Kottinger Shaull Almagor Morteza Lahijanian 225 10 0 30 Oct 2020
It's All in the Name: A Character Based Approach To Infer ReligionSocial Science Research Network (SSRN), 2020 Rochana Chaturvedi Sugat Chaturvedi 155 24 0 27 Oct 2020
Exemplary Natural Images Explain CNN Activations Better than State-of-the-Art Feature Visualization Judy Borowski Roland S. Zimmermann Judith Schepers Robert Geirhos Thomas S. A. Wallis Matthias Bethge Wieland Brendel FAtt 209 7 0 23 Oct 2020
Machine Learning Force Fields Oliver T. Unke Stefan Chmiela H. E. Sauceda M. Gastegger I. Poltavsky Kristof T. Schütt A. Tkatchenko K. Müller AI4CE 317 1,127 0 14 Oct 2020
Integrating Intrinsic and Extrinsic Explainability: The Relevance of Understanding Neural Networks for Human-Robot Interaction Tom Weber S. Wermter 80 4 0 09 Oct 2020