Unmasking Clever Hans Predictors and Assessing What Machines Really Learn

26 February 2019

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

50 / 107 papers shown

Title
AutoGPart: Intermediate Supervision Search for Generalizable 3D Part Segmentation Xueyi Liu Xiaomeng Xu Anyi Rao Chuang Gan L. Yi 3DPC 24 13 0 13 Mar 2022
Statistics and Deep Learning-based Hybrid Model for Interpretable Anomaly Detection Thabang Mathonsi Terence L van Zyl 19 0 0 25 Feb 2022
Right for the Right Latent Factors: Debiasing Generative Models via Disentanglement Xiaoting Shao Karl Stelzner Kristian Kersting CML DRL 22 3 0 01 Feb 2022
Systematic biases when using deep neural networks for annotating large catalogs of astronomical images Sanchari Dhar L. Shamir 3DPC 27 22 0 10 Jan 2022
Toward Explainable AI for Regression Models S. Letzgus Patrick Wagner Jonas Lederer Wojciech Samek Klaus-Robert Muller G. Montavon XAI 28 63 0 21 Dec 2021
Evaluating saliency methods on artificial data with different background types Céline Budding Fabian Eitel K. Ritter Stefan Haufe XAI FAtt MedIm 13 5 0 09 Dec 2021
Scaling Up Influence Functions Andrea Schioppa Polina Zablotskaia David Vilar Artem Sokolov TDI 25 90 0 06 Dec 2021
Explainable multiple abnormality classification of chest CT volumes R. Draelos Lawrence Carin MedIm 26 12 0 24 Nov 2021
A Practical guide on Explainable AI Techniques applied on Biomedical use case applications Adrien Bennetot Ivan Donadello Ayoub El Qadi M. Dragoni Thomas Frossard ... M. Trocan Raja Chatila Andreas Holzinger Artur Garcez Natalia Díaz Rodríguez XAI 24 7 0 13 Nov 2021
CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP Andreas Fürst Elisabeth Rumetshofer Johannes Lehner Viet-Hung Tran Fei Tang ... David P. Kreil Michael K Kopp G. Klambauer Angela Bitto-Nemling Sepp Hochreiter VLM CLIP 199 102 0 21 Oct 2021
Machine learning methods for prediction of cancer driver genes: a survey paper R. Andrades M. R. Mendoza 11 26 0 28 Sep 2021
Toward a Unified Framework for Debugging Concept-based Models A. Bontempelli Fausto Giunchiglia Andrea Passerini Stefano Teso 18 4 0 23 Sep 2021
Towards Zero-shot Commonsense Reasoning with Self-supervised Refinement of Language Models T. Klein Moin Nabi ReLM LRM 27 8 0 10 Sep 2021
Survey of Low-Resource Machine Translation Barry Haddow Rachel Bawden Antonio Valerio Miceli Barone Jindvrich Helcl Alexandra Birch AIMat 31 147 0 01 Sep 2021
Explaining Bayesian Neural Networks Kirill Bykov Marina M.-C. Höhne Adelaida Creosteanu Klaus-Robert Muller Frederick Klauschen Shinichi Nakajima Marius Kloft BDL AAML 28 25 0 23 Aug 2021
Interpretable SincNet-based Deep Learning for Emotion Recognition from EEG brain activity J. M. M. Torres Mirco Ravanelli Sara E. Medina-DeVilliers M. Lerner Giuseppe Riccardi 11 21 0 18 Jul 2021
Quantitative Evaluation of Explainable Graph Neural Networks for Molecular Property Prediction Jiahua Rao Shuangjia Zheng Yuedong Yang 21 46 0 01 Jul 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 27 64 0 24 Jun 2021
False perfection in machine prediction: Detecting and assessing circularity problems in machine learning Michael Hagmann Stefan Riezler 16 1 0 23 Jun 2021
Speech is Silver, Silence is Golden: What do ASVspoof-trained Models Really Learn? Nicolas M. Muller Franziska Dieckmann Pavel Czempin Roman Canals Konstantin Böttinger Jennifer Williams 28 70 0 23 Jun 2021
A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Concepts Gesina Schwalbe Bettina Finzel XAI 21 184 0 15 May 2021
Evading the Simplicity Bias: Training a Diverse Set of Models Discovers Solutions with Superior OOD Generalization Damien Teney Ehsan Abbasnejad Simon Lucey A. Hengel 23 86 0 12 May 2021
SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects Oliver T. Unke Stefan Chmiela M. Gastegger Kristof T. Schütt H. E. Sauceda K. Müller 155 245 0 01 May 2021
Why AI is Harder Than We Think Melanie Mitchell 23 95 0 26 Apr 2021
Towards a Collective Agenda on AI for Earth Science Data Analysis D. Tuia R. Roscher Jan Dirk Wegner Nathan Jacobs Xiaoxiang Zhu Gustau Camps-Valls AI4CE 39 68 0 11 Apr 2021
Evaluating explainable artificial intelligence methods for multi-label deep learning classification tasks in remote sensing Ioannis Kakogeorgiou Konstantinos Karantzalos XAI 23 118 0 03 Apr 2021
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges Cynthia Rudin Chaofan Chen Zhi Chen Haiyang Huang Lesia Semenova Chudi Zhong FaML AI4CE LRM 50 651 0 20 Mar 2021
Neural Network Attribution Methods for Problems in Geoscience: A Novel Synthetic Benchmark Dataset Antonios Mamalakis I. Ebert‐Uphoff E. Barnes OOD 23 75 0 18 Mar 2021
Explanations in Autonomous Driving: A Survey Daniel Omeiza Helena Webb Marina Jirotka Lars Kunze 11 212 0 09 Mar 2021
TimeSHAP: Explaining Recurrent Models through Sequence Perturbations João Bento Pedro Saleiro André F. Cruz Mário A. T. Figueiredo P. Bizarro FAtt AI4TS 14 88 0 30 Nov 2020
Right for the Right Concept: Revising Neuro-Symbolic Concepts by Interacting with their Explanations Wolfgang Stammer P. Schramowski Kristian Kersting FAtt 14 106 0 25 Nov 2020
Gradient Starvation: A Learning Proclivity in Neural Networks Mohammad Pezeshki Sekouba Kaba Yoshua Bengio Aaron Courville Doina Precup Guillaume Lajoie MLT 45 257 0 18 Nov 2020
It's All in the Name: A Character Based Approach To Infer Religion Rochana Chaturvedi Sugat Chaturvedi 11 23 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 34 7 0 23 Oct 2020
A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World Learning Martin Mundt Yongjun Hong Iuliia Pliushch Visvanathan Ramesh CLL 22 146 0 03 Sep 2020
Revisiting the Modifiable Areal Unit Problem in Deep Traffic Prediction with Visual Analytics Wei Zeng Chengqiao Lin Juncong Lin Jincheng Jiang Jiazhi Xia Cagatay Turkay Wei-Neng Chen 16 27 0 30 Jul 2020
Drug discovery with explainable artificial intelligence José Jiménez-Luna F. Grisoni G. Schneider 25 625 0 01 Jul 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 16 31 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 21 215 0 05 Jun 2020
Local and Global Explanations of Agent Behavior: Integrating Strategy Summaries with Saliency Maps Tobias Huber Katharina Weitz Elisabeth André Ofra Amir FAtt 16 63 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 12 4 0 12 May 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 38 82 0 17 Mar 2020
Ground Truth Evaluation of Neural Network Explanations with CLEVR-XAI L. Arras Ahmed Osman Wojciech Samek XAI AAML 21 149 0 16 Mar 2020
Evaluating Saliency Map Explanations for Convolutional Neural Networks: A User Study Ahmed Alqaraawi M. Schuessler Philipp Weiß Enrico Costanza N. Bianchi-Berthouze AAML FAtt XAI 22 197 0 03 Feb 2020
Making deep neural networks right for the right scientific reasons by interacting with their explanations P. Schramowski Wolfgang Stammer Stefano Teso Anna Brugger Xiaoting Shao Hans-Georg Luigs Anne-Katrin Mahlein Kristian Kersting 13 207 0 15 Jan 2020
On the Explanation of Machine Learning Predictions in Clinical Gait Analysis D. Slijepcevic Fabian Horst Sebastian Lapuschkin Anna-Maria Raberger Matthias Zeppelzauer Wojciech Samek C. Breiteneder W. Schöllhorn B. Horsak 22 50 0 16 Dec 2019
Towards Best Practice in Explaining Neural Network Decisions with LRP M. Kohlbrenner Alexander Bauer Shinichi Nakajima Alexander Binder Wojciech Samek Sebastian Lapuschkin 14 148 0 22 Oct 2019
Towards Explainable Artificial Intelligence Wojciech Samek K. Müller XAI 21 435 0 26 Sep 2019
Explaining and Interpreting LSTMs L. Arras Jose A. Arjona-Medina Michael Widrich G. Montavon Michael Gillhofer K. Müller Sepp Hochreiter Wojciech Samek FAtt AI4TS 16 79 0 25 Sep 2019
DeepCABAC: A Universal Compression Algorithm for Deep Neural Networks Simon Wiedemann H. Kirchhoffer Stefan Matlage Paul Haase Arturo Marbán ... Ahmed Osman D. Marpe H. Schwarz Thomas Wiegand Wojciech Samek 41 92 0 27 Jul 2019