Stop the war!Остановите войну!
for scientists:
default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDKlaus-Robert Müller
Person information
- affiliation: Technical University of Berlin, Germany
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2023
[j179]Carmen Vidaurre, K. Gurunandan
, Mina Jamshidi Idaji, Guido Nolte, Marisol Gómez, Arno Villringer, Klaus-Robert Müller, Vadim V. Nikulin:
Novel multivariate methods to track frequency shifts of neural oscillations in EEG/MEG recordings. NeuroImage 276: 120178 (2023)- [c185]Klaus-Robert Müller, Simon M. Hofmann:
Interpreting Deep Learning Models for Multi-modal Neuroimaging. BCI 2023: 1-4 - [c184]Alexander Binder, Leander Weber, Sebastian Lapuschkin, Grégoire Montavon, Klaus-Robert Müller, Wojciech Samek:
Shortcomings of Top-Down Randomization-Based Sanity Checks for Evaluations of Deep Neural Network Explanations. CVPR 2023: 16143-16152 - [c183]Ping Xiong, Thomas Schnake, Michael Gastegger, Grégoire Montavon, Klaus-Robert Müller, Shinichi Nakajima:
Relevant Walk Search for Explaining Graph Neural Networks. ICML 2023: 38301-38324 - [i119]Kirill Bykov, Klaus-Robert Müller, Marina M.-C. Höhne:
Mark My Words: Dangers of Watermarked Images in ImageNet. CoRR abs/2303.05498 (2023) - [i118]Lorenz Linhardt, Klaus-Robert Müller, Grégoire Montavon:
Preemptively Pruning Clever-Hans Strategies in Deep Neural Networks. CoRR abs/2304.05727 (2023) - [i117]Simon Letzgus, Klaus-Robert Müller:
Towards transparent and robust data-driven wind turbine power curve models. CoRR abs/2304.09835 (2023) - [i116]Lukas Muttenthaler, Robert A. Vandermeulen, Qiuyi Zhang, Thomas Unterthiner, Klaus-Robert Müller:
Set Learning for Accurate and Calibrated Models. CoRR abs/2307.02245 (2023) - 2022
- [j178]Hassan El-Hajj, Maryam Zamani, Jochen Büttner, Julius Martinetz, Oliver Eberle, Noga Shlomi, Anna Siebold, Grégoire Montavon, Klaus-Robert Müller, Holger Kantz, Matteo Valleriani:
An Ever-Expanding Humanities Knowledge Graph: The Sphaera Corpus at the Intersection of Humanities, Data Management, and Machine Learning. Datenbank-Spektrum 22(2): 153-162 (2022) - [j177]Christopher J. Anders, Leander Weber, David Neumann, Wojciech Samek, Klaus-Robert Müller, Sebastian Lapuschkin:
Finding and removing Clever Hans: Using explanation methods to debug and improve deep models. Inf. Fusion 77: 261-295 (2022) - [j176]Rick Wilming, Céline Budding, Klaus-Robert Müller, Stefan Haufe:
Scrutinizing XAI using linear ground-truth data with suppressor variables. Mach. Learn. 111(5): 1903-1923 (2022) - [j175]Ludwig Winkler, Klaus-Robert Müller, Huziel E. Sauceda:
High-fidelity molecular dynamics trajectory reconstruction with bi-directional neural networks. Mach. Learn. Sci. Technol. 3(2): 25011 (2022) - [j174]Mina Jamshidi Idaji, Juanli Zhang, Tilman Stephani, Guido Nolte, Klaus-Robert Müller, Arno Villringer, Vadim V. Nikulin:
Harmoni: A method for eliminating spurious interactions due to the harmonic components in neuronal data. NeuroImage 252: 119053 (2022) - [j173]Simon M. Hofmann, Frauke Beyer, Sebastian Lapuschkin, Ole Goltermann, Markus Loeffler, Klaus-Robert Müller, Arno Villringer, Wojciech Samek, Anja Veronica Witte:
Towards the interpretability of deep learning models for multi-modal neuroimaging: Finding structural changes of the ageing brain. NeuroImage 261: 119504 (2022) - [j172]Oliver Eberle, Jochen Büttner, Florian Kräutli, Klaus-Robert Müller, Matteo Valleriani, Grégoire Montavon:
Building and Interpreting Deep Similarity Models. IEEE Trans. Pattern Anal. Mach. Intell. 44(3): 1149-1161 (2022) - [j171]Thomas Schnake, Oliver Eberle, Jonas Lederer, Shinichi Nakajima, Kristof T. Schütt, Klaus-Robert Müller, Grégoire Montavon:
Higher-Order Explanations of Graph Neural Networks via Relevant Walks. IEEE Trans. Pattern Anal. Mach. Intell. 44(11): 7581-7596 (2022) - [j170]Ann-Kathrin Dombrowski, Christopher J. Anders, Klaus-Robert Müller, Pan Kessel:
Towards robust explanations for deep neural networks. Pattern Recognit. 121: 108194 (2022) - [j169]Tülay Adali, Rodrigo Capobianco Guido, Tin Kam Ho, Klaus-Robert Müller, Stephen C. Strother:
Interpretability, Reproducibility, and Replicability [From the Guest Editors]. IEEE Signal Process. Mag. 39(4): 5-7 (2022) - [j168]Simon Letzgus, Patrick Wagner, Jonas Lederer, Wojciech Samek, Klaus-Robert Müller, Grégoire Montavon:
Toward Explainable Artificial Intelligence for Regression Models: A methodological perspective. IEEE Signal Process. Mag. 39(4): 40-58 (2022) - [j167]Philipp Liznerski, Lukas Ruff, Robert A. Vandermeulen, Billy Joe Franks, Klaus-Robert Müller, Marius Kloft:
Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images. Trans. Mach. Learn. Res. 2022 (2022) - [c182]Seong-Whan Lee, Klaus-Robert Müller:
Welcome Message from the General Chairs. BCI 2022: 1- - [c181]Klaus-Robert Müller, A. W. Thomas, Wojciech Samek:
Deep Learning for Whole-Brain Cognitive Decoding. BCI 2022: 1-3 - [c180]Ameen Ali, Thomas Schnake, Oliver Eberle, Grégoire Montavon, Klaus-Robert Müller, Lior Wolf:
XAI for Transformers: Better Explanations through Conservative Propagation. ICML 2022: 435-451 - [c179]Ping Xiong, Thomas Schnake, Grégoire Montavon, Klaus-Robert Müller, Shinichi Nakajima:
Efficient Computation of Higher-Order Subgraph Attribution via Message Passing. ICML 2022: 24478-24495 - [c178]J. Thorben Frank, Oliver T. Unke, Klaus-Robert Müller:
So3krates: Equivariant attention for interactions on arbitrary length-scales in molecular systems. NeurIPS 2022 - [e6]Andreas Holzinger, Randy Goebel, Ruth Fong, Taesup Moon, Klaus-Robert Müller, Wojciech Samek:
xxAI - Beyond Explainable AI - International Workshop, Held in Conjunction with ICML 2020, July 18, 2020, Vienna, Austria, Revised and Extended Papers. Lecture Notes in Computer Science 13200, Springer 2022, ISBN 978-3-031-04082-5 [contents] - [i115]Ludwig Winkler, Klaus-Robert Müller, Huziel E. Sauceda:
Super-resolution in Molecular Dynamics Trajectory Reconstruction with Bi-Directional Neural Networks. CoRR abs/2201.01195 (2022) - [i114]Ann-Kathrin Dombrowski, Klaus-Robert Müller, Wolf-Christian Müller:
Automated Dissipation Control for Turbulence Simulation with Shell Models. CoRR abs/2201.02485 (2022) - [i113]Ameen Ali, Thomas Schnake, Oliver Eberle, Grégoire Montavon, Klaus-Robert Müller, Lior Wolf:
XAI for Transformers: Better Explanations through Conservative Propagation. CoRR abs/2202.07304 (2022) - [i112]Jonas Lederer, Michael Gastegger, Kristof T. Schütt, Michael Kampffmeyer, Klaus-Robert Müller, Oliver T. Unke:
Automatic Identification of Chemical Moieties. CoRR abs/2203.16205 (2022) - [i111]Oliver T. Unke, Martin Stöhr, Stefan Ganscha, Thomas Unterthiner, Hartmut Maennel, Sergii Kashubin, Daniel Ahlin, Michael Gastegger, Leonardo Medrano Sandonas, Alexandre Tkatchenko, Klaus-Robert Müller:
Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations. CoRR abs/2205.08306 (2022) - [i110]Philipp Liznerski, Lukas Ruff, Robert A. Vandermeulen, Billy Joe Franks, Klaus-Robert Müller, Marius Kloft:
Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images. CoRR abs/2205.11474 (2022) - [i109]J. Thorben Frank, Oliver T. Unke, Klaus-Robert Müller:
So3krates - Self-attention for higher-order geometric interactions on arbitrary length-scales. CoRR abs/2205.14276 (2022) - [i108]Kirill Bykov, Mayukh Deb, Dennis Grinwald, Klaus-Robert Müller, Marina M.-C. Höhne:
DORA: Exploring outlier representations in Deep Neural Networks. CoRR abs/2206.04530 (2022) - [i107]Ann-Kathrin Dombrowski, Jan E. Gerken, Klaus-Robert Müller, Pan Kessel:
Diffeomorphic Counterfactuals with Generative Models. CoRR abs/2206.05075 (2022) - [i106]Niklas Frederik Schmitz, Klaus-Robert Müller, Stefan Chmiela:
Algorithmic Differentiation for Automatized Modelling of Machine Learned Force Fields. CoRR abs/2208.12104 (2022) - [i105]Alexander Binder, Leander Weber, Sebastian Lapuschkin, Grégoire Montavon, Klaus-Robert Müller, Wojciech Samek:
Shortcomings of Top-Down Randomization-Based Sanity Checks for Evaluations of Deep Neural Network Explanations. CoRR abs/2211.12486 (2022) - [i104]Stefan Blücher, Klaus-Robert Müller, Stefan Chmiela:
Reconstructing Kernel-based Machine Learning Force Fields with Super-linear Convergence. CoRR abs/2212.12737 (2022) - [i103]Pattarawat Chormai, Jan Herrmann, Klaus-Robert Müller, Grégoire Montavon:
Disentangled Explanations of Neural Network Predictions by Finding Relevant Subspaces. CoRR abs/2212.14855 (2022) - 2021
- [j166]Mihail Bogojeski, Simeon Sauer, Franziska Horn, Klaus-Robert Müller:
Forecasting industrial aging processes with machine learning methods. Comput. Chem. Eng. 144: 107123 (2021) - [j165]Stefan Studer, Thanh Binh Bui, Christian Drescher, Alexander Hanuschkin, Ludwig Winkler, Steven Peters, Klaus-Robert Müller:
Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology. Mach. Learn. Knowl. Extr. 3(2): 392-413 (2021) - [j164]Alexander Binder, Michael Bockmayr, Miriam Hägele, Stephan Wienert, Daniel Heim, Katharina Hellweg, Masaru Ishii, Albrecht Stenzinger, Andreas Hocke, Carsten Denkert, Klaus-Robert Müller, Frederick Klauschen:
Morphological and molecular breast cancer profiling through explainable machine learning. Nat. Mach. Intell. 3(4): 355-366 (2021) - [j163]Ali Hashemi, Chang Cai, Gitta Kutyniok, Klaus-Robert Müller, Srikantan S. Nagarajan, Stefan Haufe:
Unification of sparse Bayesian learning algorithms for electromagnetic brain imaging with the majorization minimization framework. NeuroImage 239: 118309 (2021) - [j162]Vignesh Srinivasan, Csaba Rohrer, Arturo Marbán, Klaus-Robert Müller, Wojciech Samek, Shinichi Nakajima:
Robustifying models against adversarial attacks by Langevin dynamics. Neural Networks 137: 1-17 (2021) - [j161]Wojciech Samek, Grégoire Montavon, Sebastian Lapuschkin, Christopher J. Anders, Klaus-Robert Müller:
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications. Proc. IEEE 109(3): 247-278 (2021) - [j160]Lukas Ruff, Jacob R. Kauffmann, Robert A. Vandermeulen, Grégoire Montavon, Wojciech Samek, Marius Kloft, Thomas G. Dietterich, Klaus-Robert Müller:
A Unifying Review of Deep and Shallow Anomaly Detection. Proc. IEEE 109(5): 756-795 (2021) - [j159]Kai J. Miller, Klaus-Robert Müller, Dora Hermes:
Basis profile curve identification to understand electrical stimulation effects in human brain networks. PLoS Comput. Biol. 17(9) (2021) - [j158]Seul-Ki Yeom, Philipp Seegerer, Sebastian Lapuschkin, Alexander Binder, Simon Wiedemann, Klaus-Robert Müller, Wojciech Samek:
Pruning by explaining: A novel criterion for deep neural network pruning. Pattern Recognit. 115: 107899 (2021) - [j157]Felix Sattler, Klaus-Robert Müller, Wojciech Samek:
Clustered Federated Learning: Model-Agnostic Distributed Multitask Optimization Under Privacy Constraints. IEEE Trans. Neural Networks Learn. Syst. 32(8): 3710-3722 (2021) - [c177]Philipp Liznerski, Lukas Ruff, Robert A. Vandermeulen, Billy Joe Franks, Marius Kloft, Klaus-Robert Müller:
Explainable Deep One-Class Classification. ICLR 2021 - [c176]Oliver T. Unke, Mihail Bogojeski, Michael Gastegger, Mario Geiger, Tess E. Smidt, Klaus-Robert Müller:
SE(3)-equivariant prediction of molecular wavefunctions and electronic densities. NeurIPS 2021: 14434-14447 - [c175]Ali Hashemi, Yijing Gao, Chang Cai, Sanjay Ghosh, Klaus-Robert Müller, Srikantan S. Nagarajan, Stefan Haufe:
Efficient hierarchical Bayesian inference for spatio-temporal regression models in neuroimaging. NeurIPS 2021: 24855-24870 - [i102]Oliver T. Unke, Stefan Chmiela, Michael Gastegger, Kristof T. Schütt, Huziel E. Sauceda, Klaus-Robert Müller:
SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects. CoRR abs/2105.00304 (2021) - [i101]Danny Panknin, Shinichi Nakajima, Klaus-Robert Müller:
Optimal Sampling Density for Nonparametric Regression. CoRR abs/2105.11990 (2021) - [i100]Huziel E. Sauceda, Luis E. Gálvez-González, Stefan Chmiela, Lauro Oliver Paz-Borbón, Klaus-Robert Müller, Alexandre Tkatchenko:
BIGDML: Towards Exact Machine Learning Force Fields for Materials. CoRR abs/2106.04229 (2021) - [i99]Léo Andéol, Yusei Kawakami, Yuichiro Wada, Takafumi Kanamori, Klaus-Robert Müller, Grégoire Montavon:
Learning Domain Invariant Representations by Joint Wasserstein Distance Minimization. CoRR abs/2106.04923 (2021) - [i98]Christopher J. Anders, David Neumann, Wojciech Samek, Klaus-Robert Müller, Sebastian Lapuschkin:
Software for Dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAy. CoRR abs/2106.13200 (2021) - [i97]Vignesh Srinivasan, Nils Strodthoff, Jackie Ma, Alexander Binder, Klaus-Robert Müller, Wojciech Samek:
On the Robustness of Pretraining and Self-Supervision for a Deep Learning-based Analysis of Diabetic Retinopathy. CoRR abs/2106.13497 (2021) - [i96]Kirill Bykov, Marina M.-C. Höhne, Adelaida Creosteanu, Klaus-Robert Müller, Frederick Klauschen, Shinichi Nakajima, Marius Kloft:
Explaining Bayesian Neural Networks. CoRR abs/2108.10346 (2021) - [i95]Niklas W. A. Gebauer, Michael Gastegger, Stefaan S. P. Hessmann, Klaus-Robert Müller, Kristof T. Schütt:
Inverse design of 3d molecular structures with conditional generative neural networks. CoRR abs/2109.04824 (2021) - [i94]Armin W. Thomas, Ulman Lindenberger, Wojciech Samek, Klaus-Robert Müller:
Evaluating deep transfer learning for whole-brain cognitive decoding. CoRR abs/2111.01562 (2021) - [i93]Ali Hashemi, Yijing Gao, Chang Cai, Sanjay Ghosh, Klaus-Robert Müller, Srikantan S. Nagarajan, Stefan Haufe:
Efficient Hierarchical Bayesian Inference for Spatio-temporal Regression Models in Neuroimaging. CoRR abs/2111.01692 (2021) - [i92]Rick Wilming, Céline Budding, Klaus-Robert Müller, Stefan Haufe:
Scrutinizing XAI using linear ground-truth data with suppressor variables. CoRR abs/2111.07473 (2021) - [i91]Simon Letzgus, Patrick Wagner, Jonas Lederer, Wojciech Samek, Klaus-Robert Müller, Grégoire Montavon:
Toward Explainable AI for Regression Models. CoRR abs/2112.11407 (2021) - 2020
- [j156]Alexander von Lühmann, Xinge Li, Klaus-Robert Müller, David A. Boas, Meryem A. Yücel:
Improved physiological noise regression in fNIRS: A multimodal extension of the General Linear Model using temporally embedded Canonical Correlation Analysis. NeuroImage 208: 116472 (2020) - [j155]Mina Jamshidi Idaji, Klaus-Robert Müller, Guido Nolte, Burkhard Maess, Arno Villringer, Vadim V. Nikulin:
Nonlinear interaction decomposition (NID): A method for separation of cross-frequency coupled sources in human brain. NeuroImage 211: 116599 (2020) - [j154]Felix Sattler, Jackie Ma, Patrick Wagner, David Neumann, Markus Wenzel, Ralf Schäfer, Wojciech Samek, Klaus-Robert Müller, Thomas Wiegand:
Risk estimation of SARS-CoV-2 transmission from bluetooth low energy measurements. npj Digit. Medicine 3 (2020) - [j153]Jacob R. Kauffmann, Klaus-Robert Müller, Grégoire Montavon:
Towards explaining anomalies: A deep Taylor decomposition of one-class models. Pattern Recognit. 101: 107198 (2020) - [j152]Dong-Ok Won, Klaus-Robert Müller, Seong-Whan Lee:
An adaptive deep reinforcement learning framework enables curling robots with human-like performance in real-world conditions. Sci. Robotics 5(46): 9764 (2020) - [j151]Tobias Kretz, Klaus-Robert Müller, Tobias Schaeffter, Clemens Elster:
Mammography Image Quality Assurance Using Deep Learning. IEEE Trans. Biomed. Eng. 67(12): 3317-3326 (2020) - [j150]Simon Wiedemann, Klaus-Robert Müller, Wojciech Samek:
Compact and Computationally Efficient Representation of Deep Neural Networks. IEEE Trans. Neural Networks Learn. Syst. 31(3): 772-785 (2020) - [j149]Alexander Bauer, Shinichi Nakajima, Nico Görnitz, Klaus-Robert Müller:
Optimizing for Measure of Performance in Max-Margin Parsing. IEEE Trans. Neural Networks Learn. Syst. 31(7): 2680-2684 (2020) - [j148]Felix Sattler, Simon Wiedemann, Klaus-Robert Müller, Wojciech Samek:
Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data. IEEE Trans. Neural Networks Learn. Syst. 31(9): 3400-3413 (2020) - [c174]Vignesh Srinivasan, Klaus-Robert Müller, Wojciech Samek, Shinichi Nakajima:
Benign Examples: Imperceptible Changes Can Enhance Image Translation Performance. AAAI 2020: 5842-5850 - [c173]Klaus-Robert Müller:
Analysing the Changing Brain: Immediate Brain Plasticity After One Hour of BCI. BCI 2020: 1-2 - [c172]Felix Sattler, Klaus-Robert Müller, Thomas Wiegand, Wojciech Samek:
On the Byzantine Robustness of Clustered Federated Learning. ICASSP 2020: 8861-8865 - [c171]Tamer Ajaj, Klaus-Robert Müller, Gabriel Curio, Thomas Wiegand, Sebastian Bosse:
EEG-Based Assessment of Perceived Quality in Complex Natural Images. ICIP 2020: 136-140 - [c170]Lukas Ruff, Robert A. Vandermeulen, Nico Görnitz, Alexander Binder, Emmanuel Müller, Klaus-Robert Müller, Marius Kloft:
Deep Semi-Supervised Anomaly Detection. ICLR 2020 - [c169]Andreas Holzinger, Randy Goebel, Ruth Fong, Taesup Moon, Klaus-Robert Müller, Wojciech Samek:
xxAI - Beyond Explainable Artificial Intelligence. xxAI@ICML 2020: 3-10 - [c168]Grégoire Montavon, Jacob R. Kauffmann, Wojciech Samek, Klaus-Robert Müller:
Explaining the Predictions of Unsupervised Learning Models. xxAI@ICML 2020: 117-138 - [c167]Christopher J. Anders, Plamen Pasliev, Ann-Kathrin Dombrowski, Klaus-Robert Müller, Pan Kessel:
Fairwashing explanations with off-manifold detergent. ICML 2020: 314-323 - [c166]Milena T. Bagdasarian, Anna Hilsmann, Peter Eisert, Gabriel Curio, Klaus-Robert Müller, Thomas Wiegand, Sebastian Bosse:
EEG-Based Assessment of Perceived Realness in Stylized Face Images. QoMEX 2020: 1-4 - [p27]Philipp Seegerer, Alexander Binder, René Saitenmacher, Michael Bockmayr, Maximilian Alber, Philipp Jurmeister, Frederick Klauschen, Klaus-Robert Müller:
Interpretable Deep Neural Network to Predict Estrogen Receptor Status from Haematoxylin-Eosin Images. AI and ML for Digital Pathology 2020: 16-37 - [i90]Mihail Bogojeski, Simeon Sauer, Franziska Horn, Klaus-Robert Müller:
Forecasting Industrial Aging Processes with Machine Learning Methods. CoRR abs/2002.01768 (2020) - [i89]Philipp Leinen, Malte Esders, Kristof T. Schütt, Christian Wagner, Klaus-Robert Müller, F. Stefan Tautz:
Autonomous robotic nanofabrication with reinforcement learning. CoRR abs/2002.11952 (2020) - [i88]Stefan Studer, Thanh Binh Bui, Christian Drescher, Alexander Hanuschkin, Ludwig Winkler, Steven Peters, Klaus-Robert Müller:
Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology. CoRR abs/2003.05155 (2020) - [i87]Oliver Eberle, Jochen Büttner, Florian Kräutli, Klaus-Robert Müller, Matteo Valleriani, Grégoire Montavon:
Building and Interpreting Deep Similarity Models. CoRR abs/2003.05431 (2020) - [i86]Wojciech Samek, Grégoire Montavon, Sebastian Lapuschkin, Christopher J. Anders, Klaus-Robert Müller:
Toward Interpretable Machine Learning: Transparent Deep Neural Networks and Beyond. CoRR abs/2003.07631 (2020) - [i85]David Lassner, Anne Baillot, Sergej Dogadov, Klaus-Robert Müller, Shinichi Nakajima:
Automatic Identification of Types of Alterations in Historical Manuscripts. CoRR abs/2003.09136 (2020) - [i84]Felix Sattler, Jackie Ma, Patrick Wagner, David Neumann, Markus Wenzel, Ralf Schäfer, Wojciech Samek, Klaus-Robert Müller, Thomas Wiegand:
Risk Estimation of SARS-CoV-2 Transmission from Bluetooth Low Energy Measurements. CoRR abs/2004.11841 (2020) - [i83]Lukas Ruff, Robert A. Vandermeulen, Billy Joe Franks, Klaus-Robert Müller, Marius Kloft:
Rethinking Assumptions in Deep Anomaly Detection. CoRR abs/2006.00339 (2020) - [i82]Thomas Schnake, Oliver Eberle, Jonas Lederer, Shinichi Nakajima, Kristof T. Schütt, Klaus-Robert Müller, Grégoire Montavon:
XAI for Graphs: Explaining Graph Neural Network Predictions by Identifying Relevant Walks. CoRR abs/2006.03589 (2020) - [i81]Kirill Bykov, Marina M.-C. Höhne, Klaus-Robert Müller, Shinichi Nakajima, Marius Kloft:
How Much Can I Trust You? - Quantifying Uncertainties in Explaining Neural Networks. CoRR abs/2006.09000 (2020) - [i80]Jacob R. Kauffmann, Lukas Ruff, Grégoire Montavon, Klaus-Robert Müller:
The Clever Hans Effect in Anomaly Detection. CoRR abs/2006.10609 (2020) - [i79]Philipp Liznerski, Lukas Ruff, Robert A. Vandermeulen, Billy Joe Franks, Marius Kloft, Klaus-Robert Müller:
Explainable Deep One-Class Classification. CoRR abs/2007.01760 (2020) - [i78]Christopher J. Anders, Plamen Pasliev, Ann-Kathrin Dombrowski, Klaus-Robert Müller, Pan Kessel:
Fairwashing Explanations with Off-Manifold Detergent. CoRR abs/2007.09969 (2020) - [i77]Vignesh Srinivasan, Klaus-Robert Müller, Wojciech Samek, Shinichi Nakajima:
Langevin Cooling for Domain Translation. CoRR abs/2008.13723 (2020) - [i76]Lukas Ruff, Jacob R. Kauffmann, Robert A. Vandermeulen, Grégoire Montavon, Wojciech Samek, Marius Kloft, Thomas G. Dietterich, Klaus-Robert Müller:
A Unifying Review of Deep and Shallow Anomaly Detection. CoRR abs/2009.11732 (2020) - [i75]Ann-Kathrin Dombrowski, Christopher J. Anders, Klaus-Robert Müller, Pan Kessel:
Towards Robust Explanations for Deep Neural Networks. CoRR abs/2012.10425 (2020)
2010 – 2019
- 2019
- [j147]Guido Schwenk, Ralf Pabst, Klaus-Robert Müller:
Classification of structured validation data using stateless and stateful features. Comput. Commun. 138: 54-66 (2019) - [j146]Stefan Chmiela, Huziel Enoc Sauceda Felix, Igor Poltavsky, Klaus-Robert Müller, Alexandre Tkatchenko:
sGDML: Constructing accurate and data efficient molecular force fields using machine learning. Comput. Phys. Commun. 240: 38-45 (2019) - [j145]Sebastian Bosse, Sören Becker, Klaus-Robert Müller, Wojciech Samek, Thomas Wiegand:
Estimation of distortion sensitivity for visual quality prediction using a convolutional neural network. Digit. Signal Process. 91: 54-65 (2019) - [j144]Maximilian Alber, Sebastian Lapuschkin, Philipp Seegerer, Miriam Hägele, Kristof T. Schütt, Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller, Sven Dähne, Pieter-Jan Kindermans:
iNNvestigate Neural Networks! J. Mach. Learn. Res. 20: 93:1-93:8 (2019) - [j143]Joey Tianyi Zhou, Ivor W. Tsang, Shen-Shyang Ho, Klaus-Robert Müller:
N-ary decomposition for multi-class classification. Mach. Learn. 108(5): 809-830 (2019) - [j142]Carmen Vidaurre, Ander Ramos-Murguialday, Stefan Haufe, Marisol Gómez, Klaus-Robert Müller, Vadim V. Nikulin:
Enhancing sensorimotor BCI performance with assistive afferent activity: An online evaluation. NeuroImage 199: 375-386 (2019) - [j141]Alexander von Lühmann, Zois Boukouvalas, Klaus-Robert Müller, Tülay Adali:
A new blind source separation framework for signal analysis and artifact rejection in functional Near-Infrared Spectroscopy. NeuroImage 200: 72-88 (2019) - [j140]Carmen Vidaurre, Guido Nolte, Ingmar E. J. de Vries, Marisol Gómez, Tjeerd W. Boonstra, Klaus-Robert Müller, Arno Villringer, Vadim V. Nikulin:
Canonical maximization of coherence: A novel tool for investigation of neuronal interactions between two datasets. NeuroImage 201 (2019) - [c165]Alexander Bauer, Shinichi Nakajima, Nico Görnitz, Klaus-Robert Müller:
Partial Optimality of Dual Decomposition for MAP Inference in Pairwise MRFs. AISTATS 2019: 1696-1703 - [c164]Klaus-Robert Müller:
Explainable Deep Learning for Analysing Brain Data. BCI 2019: 1-2 - [c163]Leila Arras, Ahmed Osman, Klaus-Robert Müller, Wojciech Samek:
Evaluating Recurrent Neural Network Explanations. BlackboxNLP@ACL 2019: 113-126 - [c162]Patrick Wagner, Jakob Paul Morath, Arturo Zychlinsky, Klaus-Robert Müller, Wojciech Samek:
Rotation Invariant Clustering of 3D Cell Nuclei Shapes. EMBC 2019: 6022-6027 - [c161]Vignesh Srinivasan, Ercan E. Kuruoglu, Klaus-Robert Müller, Wojciech Samek, Shinichi Nakajima:
Black-Box Decision based Adversarial Attack with Symmetric α-stable Distribution. EUSIPCO 2019: 1-5 - [c160]