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Sebastian Lapuschkin
Person information
- affiliation: Fraunhofer Heinrich Hertz Institute, Berlin, Germany
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2020 – today
- 2024
- [j16]Sören Becker, Johanna Vielhaben, Marcel Ackermann, Klaus-Robert Müller
, Sebastian Lapuschkin
, Wojciech Samek
:
AudioMNIST: Exploring Explainable Artificial Intelligence for audio analysis on a simple benchmark. J. Frankl. Inst. 361(1): 418-428 (2024) - [j15]Johanna Vielhaben
, Sebastian Lapuschkin
, Grégoire Montavon
, Wojciech Samek
:
Explainable AI for time series via Virtual Inspection Layers. Pattern Recognit. 150: 110309 (2024) - [c20]Maximilian Dreyer, Frederik Pahde, Christopher J. Anders, Wojciech Samek, Sebastian Lapuschkin:
From Hope to Safety: Unlearning Biases of Deep Models via Gradient Penalization in Latent Space. AAAI 2024: 21046-21054 - [i55]Anna Hedström, Leander Weber, Sebastian Lapuschkin, Marina M.-C. Höhne:
Sanity Checks Revisited: An Exploration to Repair the Model Parameter Randomisation Test. CoRR abs/2401.06465 (2024) - [i54]Florian Bley, Sebastian Lapuschkin, Wojciech Samek, Grégoire Montavon:
Explaining Predictive Uncertainty by Exposing Second-Order Effects. CoRR abs/2401.17441 (2024) - [i53]Reduan Achtibat, Sayed Mohammad Vakilzadeh Hatefi, Maximilian Dreyer, Aakriti Jain, Thomas Wiegand, Sebastian Lapuschkin, Wojciech Samek:
AttnLRP: Attention-Aware Layer-wise Relevance Propagation for Transformers. CoRR abs/2402.05602 (2024) - [i52]Galip Ümit Yolcu, Thomas Wiegand, Wojciech Samek, Sebastian Lapuschkin:
DualView: Data Attribution from the Dual Perspective. CoRR abs/2402.12118 (2024) - [i51]Maximilian Dreyer, Erblina Purelku, Johanna Vielhaben, Wojciech Samek, Sebastian Lapuschkin:
PURE: Turning Polysemantic Neurons Into Pure Features by Identifying Relevant Circuits. CoRR abs/2404.06453 (2024) - [i50]Dilyara Bareeva, Maximilian Dreyer, Frederik Pahde, Wojciech Samek, Sebastian Lapuschkin:
Reactive Model Correction: Mitigating Harm to Task-Relevant Features via Conditional Bias Suppression. CoRR abs/2404.09601 (2024) - [i49]Christian Tinauer, Anna Damulina, Maximilian Sackl, Martin Soellradl, Reduan Achtibat, Maximilian Dreyer, Frederik Pahde, Sebastian Lapuschkin, Reinhold Schmidt, Stefan Ropele, Wojciech Samek, Christian Langkammer:
Explainable concept mappings of MRI: Revealing the mechanisms underlying deep learning-based brain disease classification. CoRR abs/2404.10433 (2024) - [i48]Anna Hedström, Leander Weber, Sebastian Lapuschkin, Marina M.-C. Höhne:
A Fresh Look at Sanity Checks for Saliency Maps. CoRR abs/2405.02383 (2024) - [i47]Laura Kopf, Philine Lou Bommer, Anna Hedström, Sebastian Lapuschkin, Marina M.-C. Höhne, Kirill Bykov:
CoSy: Evaluating Textual Explanations of Neurons. CoRR abs/2405.20331 (2024) - 2023
- [j14]Leander Weber
, Sebastian Lapuschkin
, Alexander Binder
, Wojciech Samek
:
Beyond explaining: Opportunities and challenges of XAI-based model improvement. Inf. Fusion 92: 154-176 (2023) - [j13]Anna Hedström, Leander Weber, Daniel Krakowczyk, Dilyara Bareeva, Franz Motzkus, Wojciech Samek, Sebastian Lapuschkin, Marina M.-C. Höhne:
Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond. J. Mach. Learn. Res. 24: 34:1-34:11 (2023) - [j12]Reduan Achtibat
, Maximilian Dreyer, Ilona Eisenbraun, Sebastian Bosse, Thomas Wiegand, Wojciech Samek
, Sebastian Lapuschkin
:
From attribution maps to human-understandable explanations through Concept Relevance Propagation. Nat. Mac. Intell. 5(9): 1006-1019 (2023) - [j11]Anna Hedström, Philine Lou Bommer, Kristoffer Knutsen Wickstrøm, Wojciech Samek, Sebastian Lapuschkin, Marina M.-C. Höhne:
The Meta-Evaluation Problem in Explainable AI: Identifying Reliable Estimators with MetaQuantus. Trans. Mach. Learn. Res. 2023 (2023) - [c19]Annika Frommholz
, Fabian Seipel
, Sebastian Lapuschkin
, Wojciech Samek
, Johanna Vielhaben
:
XAI-based Comparison of Audio Event Classifiers with different Input Representations. CBMI 2023: 126-132 - [c18]Frederik Pahde, Galip Ümit Yolcu, Alexander Binder, Wojciech Samek, Sebastian Lapuschkin:
Optimizing Explanations by Network Canonization and Hyperparameter Search. CVPR Workshops 2023: 3819-3828 - [c17]Maximilian Dreyer, Reduan Achtibat, Thomas Wiegand, Wojciech Samek, Sebastian Lapuschkin:
Revealing Hidden Context Bias in Segmentation and Object Detection through Concept-specific Explanations. CVPR Workshops 2023: 3829-3839 - [c16]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 - [c15]Daniel G. Krakowczyk
, Paul Prasse
, David R. Reich
, Sebastian Lapuschkin
, Tobias Scheffer
, Lena A. Jäger
:
Bridging the Gap: Gaze Events as Interpretable Concepts to Explain Deep Neural Sequence Models. ETRA 2023: 3:1-3:8 - [c14]Karam Dawoud, Wojciech Samek, Peter Eisert, Sebastian Lapuschkin, Sebastian Bosse:
Human-Centered Evaluation of XAI Methods. ICDM (Workshops) 2023: 912-921 - [c13]Frederik Pahde, Maximilian Dreyer, Wojciech Samek, Sebastian Lapuschkin
:
Reveal to Revise: An Explainable AI Life Cycle for Iterative Bias Correction of Deep Models. MICCAI (2) 2023: 596-606 - [i46]Anna Hedström, Philine Lou Bommer, Kristoffer K. Wickstrøm, Wojciech Samek, Sebastian Lapuschkin, Marina M.-C. Höhne:
The Meta-Evaluation Problem in Explainable AI: Identifying Reliable Estimators with MetaQuantus. CoRR abs/2302.07265 (2023) - [i45]Johanna Vielhaben, Sebastian Lapuschkin, Grégoire Montavon, Wojciech Samek:
Explainable AI for Time Series via Virtual Inspection Layers. CoRR abs/2303.06365 (2023) - [i44]Frederik Pahde, Maximilian Dreyer, Wojciech Samek, Sebastian Lapuschkin:
Reveal to Revise: An Explainable AI Life Cycle for Iterative Bias Correction of Deep Models. CoRR abs/2303.12641 (2023) - [i43]Daniel G. Krakowczyk, Paul Prasse
, David R. Reich, Sebastian Lapuschkin, Tobias Scheffer, Lena A. Jäger:
Bridging the Gap: Gaze Events as Interpretable Concepts to Explain Deep Neural Sequence Models. CoRR abs/2304.13536 (2023) - [i42]Annika Frommholz, Fabian Seipel, Sebastian Lapuschkin, Wojciech Samek, Johanna Vielhaben:
XAI-based Comparison of Input Representations for Audio Event Classification. CoRR abs/2304.14019 (2023) - [i41]Maximilian Dreyer, Frederik Pahde, Christopher J. Anders, Wojciech Samek, Sebastian Lapuschkin:
From Hope to Safety: Unlearning Biases of Deep Models by Enforcing the Right Reasons in Latent Space. CoRR abs/2308.09437 (2023) - [i40]Leander Weber, Jim Berend, Alexander Binder, Thomas Wiegand, Wojciech Samek, Sebastian Lapuschkin:
Layer-wise Feedback Propagation. CoRR abs/2308.12053 (2023) - [i39]Karam Dawoud, Wojciech Samek, Peter Eisert, Sebastian Lapuschkin, Sebastian Bosse:
Human-Centered Evaluation of XAI Methods. CoRR abs/2310.07534 (2023) - [i38]Gabriel Nobis, Marco Aversa, Maximilian Springenberg, Michael Detzel, Stefano Ermon, Shinichi Nakajima, Roderick Murray-Smith, Sebastian Lapuschkin, Christoph Knochenhauer, Luis Oala, Wojciech Samek:
Generative Fractional Diffusion Models. CoRR abs/2310.17638 (2023) - [i37]Maximilian Dreyer, Reduan Achtibat, Wojciech Samek, Sebastian Lapuschkin:
Understanding the (Extra-)Ordinary: Validating Deep Model Decisions with Prototypical Concept-based Explanations. CoRR abs/2311.16681 (2023) - 2022
- [j10]Djordje Slijepcevic, Fabian Horst, Sebastian Lapuschkin
, Brian Horsak
, Anna-Maria Raberger, Andreas Kranzl
, Wojciech Samek, Christian Breiteneder, Wolfgang Immanuel Schöllhorn, Matthias Zeppelzauer:
Explaining Machine Learning Models for Clinical Gait Analysis. ACM Trans. Comput. Heal. 3(2): 14:1-14:27 (2022) - [j9]Jiamei Sun
, Sebastian Lapuschkin
, Wojciech Samek
, Alexander Binder
:
Explain and improve: LRP-inference fine-tuning for image captioning models. Inf. Fusion 77: 233-246 (2022) - [j8]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) - [j7]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) - [c12]Sami Ede, Serop Baghdadlian, Leander Weber
, An Nguyen, Dario Zanca
, Wojciech Samek
, Sebastian Lapuschkin
:
Explain to Not Forget: Defending Against Catastrophic Forgetting with XAI. CD-MAKE 2022: 1-18 - [c11]Daniel Krakowczyk, David R. Reich, Paul Prasse, Sebastian Lapuschkin
, Tobias Scheffer, Lena A. Jäger:
Selection of XAI Methods Matters: Evaluation of Feature Attribution Methods for Oculomotoric Biometric Identification. Gaze Meets ML 2022: 66-97 - [c10]Franz Motzkus
, Leander Weber, Sebastian Lapuschkin
:
Measurably Stronger Explanation Reliability Via Model Canonization. ICIP 2022: 516-520 - [i36]Frederik Pahde, Leander Weber, Christopher J. Anders, Wojciech Samek, Sebastian Lapuschkin:
PatClArC: Using Pattern Concept Activation Vectors for Noise-Robust Model Debugging. CoRR abs/2202.03482 (2022) - [i35]Franz Motzkus, Leander Weber, Sebastian Lapuschkin:
Measurably Stronger Explanation Reliability via Model Canonization. CoRR abs/2202.06621 (2022) - [i34]Anna Hedström, Leander Weber, Dilyara Bareeva, Franz Motzkus, Wojciech Samek, Sebastian Lapuschkin, Marina M.-C. Höhne:
Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations. CoRR abs/2202.06861 (2022) - [i33]Leander Weber, Sebastian Lapuschkin, Alexander Binder, Wojciech Samek:
Beyond Explaining: Opportunities and Challenges of XAI-Based Model Improvement. CoRR abs/2203.08008 (2022) - [i32]Michael Gerstenberger, Sebastian Lapuschkin, Peter Eisert, Sebastian Bosse:
But that's not why: Inference adjustment by interactive prototype deselection. CoRR abs/2203.10087 (2022) - [i31]Sami Ede, Serop Baghdadlian, Leander Weber, An Nguyen, Dario Zanca, Wojciech Samek, Sebastian Lapuschkin:
Explain to Not Forget: Defending Against Catastrophic Forgetting with XAI. CoRR abs/2205.01929 (2022) - [i30]Reduan Achtibat, Maximilian Dreyer, Ilona Eisenbraun, Sebastian Bosse, Thomas Wiegand, Wojciech Samek, Sebastian Lapuschkin:
From "Where" to "What": Towards Human-Understandable Explanations through Concept Relevance Propagation. CoRR abs/2206.03208 (2022) - [i29]Maximilian Dreyer, Reduan Achtibat, Thomas Wiegand, Wojciech Samek, Sebastian Lapuschkin:
Revealing Hidden Context Bias in Segmentation and Object Detection through Concept-specific Explanations. CoRR abs/2211.11426 (2022) - [i28]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) - [i27]Fabian Horst, Djordje Slijepcevic, Matthias Zeppelzauer, Anna-Maria Raberger, Sebastian Lapuschkin, Wojciech Samek, Wolfgang Immanuel Schöllhorn, Christian Breiteneder, Brian Horsak:
Explaining automated gender classification of human gait. CoRR abs/2211.17015 (2022) - [i26]Djordje Slijepcevic, Fabian Horst, Marvin Simak, Sebastian Lapuschkin, Anna-Maria Raberger, Wojciech Samek, Christian Breiteneder, Wolfgang Immanuel Schöllhorn, Matthias Zeppelzauer, Brian Horsak:
Explaining machine learning models for age classification in human gait analysis. CoRR abs/2211.17016 (2022) - [i25]Frederik Pahde, Galip Ümit Yolcu, Alexander Binder
, Wojciech Samek, Sebastian Lapuschkin:
Optimizing Explanations by Network Canonization and Hyperparameter Search. CoRR abs/2211.17174 (2022) - 2021
- [j6]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) - [j5]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) - [i24]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) - [i23]Daniel Becking, Maximilian Dreyer, Wojciech Samek, Karsten Müller, Sebastian Lapuschkin:
ECQx: Explainability-Driven Quantization for Low-Bit and Sparse DNNs. CoRR abs/2109.04236 (2021) - 2020
- [c9]Daniel Becking
, Maximilian Dreyer, Wojciech Samek
, Karsten Müller
, Sebastian Lapuschkin
:
ECQ x: Explainability-Driven Quantization for Low-Bit and Sparse DNNs. xxAI@ICML 2020: 271-296 - [c8]Gary S. W. Goh, Sebastian Lapuschkin
, Leander Weber, Wojciech Samek, Alexander Binder
:
Understanding Integrated Gradients with SmoothTaylor for Deep Neural Network Attribution. ICPR 2020: 4949-4956 - [c7]Jiamei Sun, Sebastian Lapuschkin
, Wojciech Samek, Yunqing Zhao, Ngai-Man Cheung, Alexander Binder
:
Explanation-Guided Training for Cross-Domain Few-Shot Classification. ICPR 2020: 7609-7616 - [c6]Maximilian Kohlbrenner, Alexander Bauer, Shinichi Nakajima, Alexander Binder
, Wojciech Samek, Sebastian Lapuschkin
:
Towards Best Practice in Explaining Neural Network Decisions with LRP. IJCNN 2020: 1-7 - [i22]Jiamei Sun, Sebastian Lapuschkin
, Wojciech Samek, Alexander Binder
:
Understanding Image Captioning Models beyond Visualizing Attention. CoRR abs/2001.01037 (2020) - [i21]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) - [i20]Gary S. W. Goh, Sebastian Lapuschkin, Leander Weber
, Wojciech Samek, Alexander Binder
:
Understanding Integrated Gradients with SmoothTaylor for Deep Neural Network Attribution. CoRR abs/2004.10484 (2020) - [i19]Jiamei Sun, Sebastian Lapuschkin, Wojciech Samek, Yunqing Zhao, Ngai-Man Cheung, Alexander Binder
:
Explanation-Guided Training for Cross-Domain Few-Shot Classification. CoRR abs/2007.08790 (2020)
2010 – 2019
- 2019
- [b1]Sebastian Lapuschkin:
Opening the machine learning black box with Layer-wise Relevance Propagation. Technical University of Berlin, Germany, 2019 - [j4]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) - [p1]Grégoire Montavon, Alexander Binder
, Sebastian Lapuschkin
, Wojciech Samek, Klaus-Robert Müller:
Layer-Wise Relevance Propagation: An Overview. Explainable AI 2019: 193-209 - [i18]Sebastian Lapuschkin, Stephan Wäldchen, Alexander Binder, Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller:
Unmasking Clever Hans Predictors and Assessing What Machines Really Learn. CoRR abs/1902.10178 (2019) - [i17]Miriam Hägele, Philipp Seegerer
, Sebastian Lapuschkin
, Michael Bockmayr, Wojciech Samek, Frederick Klauschen, Klaus-Robert Müller, Alexander Binder:
Resolving challenges in deep learning-based analyses of histopathological images using explanation methods. CoRR abs/1908.06943 (2019) - [i16]Maximilian Kohlbrenner, Alexander Bauer, Shinichi Nakajima, Alexander Binder
, Wojciech Samek, Sebastian Lapuschkin
:
Towards best practice in explaining neural network decisions with LRP. CoRR abs/1910.09840 (2019) - [i15]Fabian Horst, Djordje Slijepcevic, Sebastian Lapuschkin
, Anna-Maria Raberger, Matthias Zeppelzauer, Wojciech Samek, Christian Breiteneder, Wolfgang Immanuel Schöllhorn, Brian Horsak:
On the Understanding and Interpretation of Machine Learning Predictions in Clinical Gait Analysis Using Explainable Artificial Intelligence. CoRR abs/1912.07737 (2019) - [i14]Seul-Ki Yeom, Philipp Seegerer, Sebastian Lapuschkin
, Simon Wiedemann, Klaus-Robert Müller, Wojciech Samek:
Pruning by Explaining: A Novel Criterion for Deep Neural Network Pruning. CoRR abs/1912.08881 (2019) - [i13]Christopher J. Anders, Talmaj Marinc, David Neumann, Wojciech Samek, Klaus-Robert Müller, Sebastian Lapuschkin
:
Analyzing ImageNet with Spectral Relevance Analysis: Towards ImageNet un-Hans'ed. CoRR abs/1912.11425 (2019) - 2018
- [i12]Sören Becker, Marcel Ackermann, Sebastian Lapuschkin, Klaus-Robert Müller, Wojciech Samek:
Interpreting and Explaining Deep Neural Networks for Classification of Audio Signals. CoRR abs/1807.03418 (2018) - [i11]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! CoRR abs/1808.04260 (2018) - [i10]Fabian Horst, Sebastian Lapuschkin, Wojciech Samek, Klaus-Robert Müller, Wolfgang Immanuel Schöllhorn:
What is Unique in Individual Gait Patterns? Understanding and Interpreting Deep Learning in Gait Analysis. CoRR abs/1808.04308 (2018) - 2017
- [j3]Grégoire Montavon, Sebastian Lapuschkin
, Alexander Binder
, Wojciech Samek, Klaus-Robert Müller
:
Explaining nonlinear classification decisions with deep Taylor decomposition. Pattern Recognit. 65: 211-222 (2017) - [j2]Wojciech Samek
, Alexander Binder
, Grégoire Montavon, Sebastian Lapuschkin
, Klaus-Robert Müller
:
Evaluating the Visualization of What a Deep Neural Network Has Learned. IEEE Trans. Neural Networks Learn. Syst. 28(11): 2660-2673 (2017) - [c5]Vignesh Srinivasan, Sebastian Lapuschkin
, Cornelius Hellge, Klaus-Robert Müller
, Wojciech Samek:
Interpretable human action recognition in compressed domain. ICASSP 2017: 1692-1696 - [c4]Wojciech Samek, Alexander Binder
, Sebastian Lapuschkin
, Klaus-Robert Müller
:
Understanding and Comparing Deep Neural Networks for Age and Gender Classification. ICCV Workshops 2017: 1629-1638 - [i9]Sebastian Lapuschkin, Alexander Binder, Klaus-Robert Müller, Wojciech Samek:
Understanding and Comparing Deep Neural Networks for Age and Gender Classification. CoRR abs/1708.07689 (2017) - 2016
- [j1]Sebastian Lapuschkin, Alexander Binder, Grégoire Montavon, Klaus-Robert Müller, Wojciech Samek:
The LRP Toolbox for Artificial Neural Networks. J. Mach. Learn. Res. 17: 114:1-114:5 (2016) - [c3]Sebastian Lapuschkin
, Alexander Binder
, Grégoire Montavon, Klaus-Robert Müller
, Wojciech Samek:
Analyzing Classifiers: Fisher Vectors and Deep Neural Networks. CVPR 2016: 2912-2920 - [c2]Alexander Binder
, Grégoire Montavon, Sebastian Lapuschkin
, Klaus-Robert Müller
, Wojciech Samek:
Layer-Wise Relevance Propagation for Neural Networks with Local Renormalization Layers. ICANN (2) 2016: 63-71 - [c1]Sebastian Bach
, Alexander Binder
, Klaus-Robert Müller
, Wojciech Samek:
Controlling explanatory heatmap resolution and semantics via decomposition depth. ICIP 2016: 2271-2275 - [i8]Sebastian Bach, Alexander Binder, Klaus-Robert Müller, Wojciech Samek:
Controlling Explanatory Heatmap Resolution and Semantics via Decomposition Depth. CoRR abs/1603.06463 (2016) - [i7]Alexander Binder, Grégoire Montavon, Sebastian Bach, Klaus-Robert Müller, Wojciech Samek:
Layer-wise Relevance Propagation for Neural Networks with Local Renormalization Layers. CoRR abs/1604.00825 (2016) - [i6]Irene Sturm, Sebastian Bach, Wojciech Samek, Klaus-Robert Müller:
Interpretable Deep Neural Networks for Single-Trial EEG Classification. CoRR abs/1604.08201 (2016) - [i5]Wojciech Samek, Grégoire Montavon, Alexander Binder, Sebastian Lapuschkin, Klaus-Robert Müller:
Interpreting the Predictions of Complex ML Models by Layer-wise Relevance Propagation. CoRR abs/1611.08191 (2016) - 2015
- [i4]Wojciech Samek, Alexander Binder, Grégoire Montavon, Sebastian Bach, Klaus-Robert Müller:
Evaluating the visualization of what a Deep Neural Network has learned. CoRR abs/1509.06321 (2015) - [i3]Sebastian Bach, Alexander Binder, Grégoire Montavon, Klaus-Robert Müller, Wojciech Samek:
Analyzing Classifiers: Fisher Vectors and Deep Neural Networks. CoRR abs/1512.00172 (2015) - [i2]Grégoire Montavon, Sebastian Bach, Alexander Binder, Wojciech Samek, Klaus-Robert Müller:
Explaining NonLinear Classification Decisions with Deep Taylor Decomposition. CoRR abs/1512.02479 (2015) - 2014
- [i1]Guido Schwenk, Sebastian Bach:
Detecting Behavioral and Structural Anomalies in MediaCloud Applications. CoRR abs/1409.8035 (2014)
Coauthor Index
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