Alexander Binder

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Klaus-Robert MüllerTU Berlin & Korea University & Google Deepmind & Max Planck Institute for Informatics, GermanyVerified email at tu-berlin.de
Wojciech SamekProfessor at TU Berlin, Head of AI Department at Fraunhofer HHI, BIFOLD FellowVerified email at tu-berlin.de
Sebastian LapuschkinHead of Explainable AI, Fraunhofer Heinrich Hertz InstituteVerified email at hhi.fraunhofer.de
Grégoire MontavonFreie Universität BerlinVerified email at fu-berlin.de
Marius KloftRPTU Kaiserslautern-LandauVerified email at cs.uni-kl.de
Frederick KlauschenInstitute of Pathology, University of Munich (LMU)Verified email at lmu.de
Nico GoernitzCTO @ MorphAIs | former PostDoc @ TU-BerlinVerified email at tu-berlin.de
Motoaki KawanabeSenior Researcher, Advanced Telecommunications Research Institute InternationalVerified email at atr.jp
Lukas RuffAignosticsVerified email at aignostics.com
Yuval EloviciHead of Cyber@BGU, Director of Telekom Innovation Laboratories at BGU, Ben Gurion UniversityVerified email at bgu.ac.il
Sun JiameiSingapore University of Technology and DesignVerified email at mymail.sutd.edu.sg
Penny ChongIBM Research, SingaporeVerified email at ibm.com
Emmanuel MüllerProfessor of Computer Science, Technical University of DortmundVerified email at cs.tu-dortmund.de
Robert A. VandermeulenTechnische Universität Berlin & BIFOLDVerified email at tu-berlin.de
Yi Xiang Marcus TanSingapore University of Technology and DesignVerified email at mymail.sutd.edu.sg
Shinichi NakajimaMachine Learning Group, Technische Universität BerlinVerified email at tu-berlin.de
Shoaib Ahmed SiddiquiUniversity of CambridgeVerified email at cam.ac.uk
Lucas DeeckeUniversity of EdinburghVerified email at ed.ac.uk
Michael BockmayrVerified email at uke.de
Philipp SeegererAignostics GmbHVerified email at tu-berlin.de

Alexander Binder

Associate Professor, Singapore Institute of Technology (SIT)

Verified email at singaporetech.edu.sg

Explainable Deep Learning XAI Aspects of Machine Learning


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On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation S Bach, A Binder, G Montavon, F Klauschen, KR Müller, W Samek PLoS ONE doi: 10.1371/journal.pone.0130140, 2015	4069	2015
Deep one-class classification L Ruff, R Vandermeulen, N Goernitz, L Deecke, SA Siddiqui, A Binder, ... International Conference on Machine Learning, 4393-4402, 2018	1827	2018
Explaining nonlinear classification decisions with deep taylor decomposition G Montavon, S Lapuschkin, A Binder, W Samek, KR Müller Pattern Recognition 65, 211-222, 2017	1418	2017
Evaluating the visualization of what a deep neural network has learned W Samek, A Binder, G Montavon, S Lapuschkin, KR Müller IEEE transactions on neural networks and learning systems, 2017	1183	2017
Unmasking Clever Hans predictors and assessing what machines really learn S Lapuschkin, S Wäldchen, A Binder, G Montavon, W Samek, KR Müller Nature Communications 10 (1), 1096, 2019	992	2019
Layer-wise relevance propagation: an overview G Montavon, A Binder, S Lapuschkin, W Samek, KR Müller Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, 193-209, 2019	651	2019
Deep Semi-Supervised Anomaly Detection L Ruff, RA Vandermeulen, N Görnitz, A Binder, E Müller, KR Müller, ... arXiv preprint arXiv:1906.02694, 2019	512	2019
Layer-wise relevance propagation for neural networks with local renormalization layers A Binder, G Montavon, S Lapuschkin, KR Müller, W Samek International Conference on Artificial Neural Networks, 63-71, 2016	405	2016
The SHOGUN machine learning toolbox S Sonnenburg, G Rätsch, S Henschel, C Widmer, J Behr, A Zien, F Bona, ... The Journal of Machine Learning Research 11, 1799-1802, 2010	374	2010
Analyzing classifiers: fisher vectors and deep neural networks S Lapuschkin, A Binder, G Montavon, KR Muller, W Samek Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2016	227	2016
Layer-wise Relevance Propagation for Deep Neural Network Architectures A Binder, S Bach, G Montavon, KR Müller, W Samek Information Science and Applications (ICISA) 2016, 2016	182	2016
Pruning by explaining: A novel criterion for deep neural network pruning SK Yeom, P Seegerer, S Lapuschkin, A Binder, S Wiedemann, KR Müller, ... Pattern Recognition, 107899, 2021	166	2021
The LRP toolbox for artificial neural networks S Lapuschkin, A Binder, G Montavon, KR Müller, W Samek The Journal of Machine Learning Research 17 (1), 3938-3942, 2016	163	2016
Understanding and Comparing Deep Neural Networks for Age and Gender Classification S Lapuschkin, A Binder, KR Müller, W Samek of the IEEE Conference on Computer Vision and Pattern Recognition, 1629-1638, 2017	158	2017
Scoring of tumor-infiltrating lymphocytes: From visual estimation to machine learning F Klauschen, KR Müller, A Binder, M Bockmayr, M Hägele, P Seegerer, ... Seminars in cancer biology 52, 151-157, 2018	141	2018
Towards best practice in explaining neural network decisions with LRP M Kohlbrenner, A Bauer, S Nakajima, A Binder, W Samek, S Lapuschkin 2020 International Joint Conference on Neural Networks (IJCNN), 1-7, 2020	137	2020
Resolving challenges in deep learning-based analyses of histopathological images using explanation methods M Hägele, P Seegerer, S Lapuschkin, M Bockmayr, W Samek, ... Scientific Reports 10 (1), 1-12, 2020	125	2020
Morphological and molecular breast cancer profiling through explainable machine learning A Binder, M Bockmayr, M Hägele, S Wienert, D Heim, K Hellweg, M Ishii, ... Nature Machine Intelligence 3 (4), 355-366, 2021	94	2021
DeepClue: Visual Interpretation of Text-based Deep Stock Prediction L Shi, Z Teng, L Wang, Y Zhang, A Binder IEEE Transactions on Knowledge and Data Engineering, 2018	82	2018
Explanation-guided training for cross-domain few-shot classification J Sun, S Lapuschkin, W Samek, Y Zhao, NM Cheung, A Binder 2020 25th International Conference on Pattern Recognition (ICPR), 7609-7616, 2021	73	2021

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