Prof. Dr. Wojciech Samek | Department of Artificial Intelligence

			Fraunhofer Heinrich Hertz Institute
			Prof. Dr. Wojciech Samek
			Head of AI Department, Fraunhofer HHI Professor for ML & Communications, TU Berlin
			Fraunhofer Heinrich Hertz Institute HHI Einsteinufer 37 10587 Berlin Germany Tel: +49 30 31002-417 Fax: +49 30 31002-190

			[ Bio \| Press \| Events \| Research \| Teaching \| Publications \| Activities ]

Short Bio

Wojciech Samek is a professor in the Department of Electrical Engineering and Computer Science at the Technical University of Berlin and is jointly heading the Department of Artificial Intelligence at Fraunhofer Heinrich Hertz Institute (HHI), Berlin, Germany. He studied computer science at Humboldt University of Berlin, Heriot-Watt University and University of Edinburgh and received the Dr. rer. nat. degree with distinction (summa cum laude) from the Technical University of Berlin in 2014. During his studies he was awarded scholarships from the German Academic Scholarship Foundation and the DFG Research Training Group GRK 1589/1, and was a visiting researcher at NASA Ames Research Center, Mountain View, USA. Dr. Samek is a Fellow at the BIFOLD - Berlin Institute for the Foundation of Learning and Data and the ELLIS Unit Berlin, and Principal Investigator at the DFG Research Unit DeSBi and the DFG Graduate School BIOQIC. Furthermore, he an elected member and the Germany's Platform for Artificial Intelligence, as well as member of the Scientific Advisory Board Member of the Center of Excellence in Artificial Intelligence at AGH University of Krakow and the Executive Boards of the Helmholtz Einstein School in Data Science HEIBRiDS and the DAAD Konrad Zuse School ELIZA. He is recipient of multiple best paper awards, including the 2020 Pattern Recognition Best Paper Award and the 2022 Digital Signal Processing Best Paper Prize, and part of the expert group developing the ISO/IEC MPEG-17 NNC standard. He edited books on Explainable AI (2019) and xxAI – Beyond explainable AI (2022), was senior editor of IEEE TNNLS, associate editor of Pattern Recognition, Digital Signal Processing, PLoS ONE, area chair at NeurIPS, ICML and NAACL, program chair at IEEE MLSP 2023 and organizer of various special sessions, workshops and tutorials on topics such as explainable AI, neural network compression, and federated learning. Dr. Samek has co-authored more than 200 peer-reviewed journal and conference papers; some of them listed as ESI Hot (top 0.1%) or Highly Cited Papers (top 1%).

Our work in the press

Transparente KI gibt Entscheidungswege preis

Wo Deutschland in der Künstlichen Intelligenz vorn ist

Gefährliche Male? Hautscan-Apps mit KI im Vergleich

Künstliche Intelligenz und die Suche nach Erklärbarkeit

KI sicher zu machen, ist ein komplexeres Problem

Wie KI-Systeme in der Praxis prüfbar gemacht werden können

Fraunhofer HHI: Mehr Power für Erklärbare KI

Wie sich KI-Entscheidungen überprüfen lassen

Forscher testen künstliche Intelligenzen auf Intelligenz

Künstliche Intelligenz schummelt öfter als gedacht

Studie: 50 Prozent der Systeme für Künstliche Intelligenz schummeln

Künstliche Intelligenz auf der falschen Spur

KI to go: Wie sich neuronale Netze smart komprimieren lassen

Computer says no: why making AIs fair, accountable and transparent is crucial

Einblicke in die 'Black Box' machinelle Lernsysteme

Wie Forscher dem Computer beim Denken zusehen

So verändert Machine Learning die Wirtschaft

Die rätselhafte Gedankenwelt eines Computers

Brainlike computers are a black box. Scientists are finally peering inside

Events

2023-09-18 Talk at ECML PKDD'23 Workshop on "Simplification, Compression, Efficiency and Frugality for Artificial intelligence", Torino, Italy.
2023-08-09 Talk at Human-Centered AI & Security Seminar Series, virtual talk.
2023-07-17 Tutorial at Bayreuth Summer School of Philosophy and Computer Science, Bayreuth, Germany.
2023-06-19 Talk at IEEE CVPR'23 Safe Artificial Intelligence for All Domains Workshop, Vancouver, Canada.
2023-04-26 Keynote at Polish Conference on Artificial Intelligence, Lodz, Poland.
2023-03-28 Keynote at Explainability in Machine Learning, Tübingen, Germany.
2023-03-27 Talk at WhiteBox Milestone Conference, Darmstadt, Germany.
2023-03-08 Tutorial at Spring School "Ethos+Tekhnè : A new generation of AI researchers" , Pisa, Italy.
2022-12-14 Tutorial at Intelligent Sensing Winter School, virtual event.
2022-12-06 Talk at Max Planck School of Cognition Academy, Berlin, Germany.
2022-11-25 Talk at Symposium of the German National Academy of Sciences - Leopoldina, Halle, Germany.
2022-10-10 Tutorial at 7th Summer School on Data Science (SSDS-2022), virtual event.
2022-07-25 Tutorial at 6th International Gran Canaria School on Deep Learning, Gran Canaria, Spain.
2022-06-26 Tutorial at 24th International Conference on Human-Computer Interaction, virtual event.
2022-04-20 Our book "xxAI - Beyond Explainable AI" is available
2021-09-27 IEEE SPAWC 2021 Tutorial on "Wireless Federated Learning", Lucca, Italy.
2021-09-19 IEEE ICIP 2021 Special Session on "Neural Network Compression and Compact Deep Features: From Methods to Standards", Anchorage, USA.
2021-07-23 Tutorial "Toward Explainable AI" at ICML 2021 Workshop on "Theoretic Foundation, Criticism, and Application Trend of Explainable AI", virtual event.
2021-06-20 Talk at IEEE CVPR 2021 Workshop on "Interpretable Machine Learning for Computer Vision".
2021-03-25 Start of the Workshop Series on "Trustworthy AI" at the AI for Good Global Summit.
2020-12-11 IEEE GLOBECOM 2020 Tutorial on "Distributed Deep Learning: Concepts, Methods & Applications in Wireless Networks", Taipei, Taiwan.
2020-10-19 Talk at ACM CIKM'20 Workshop on "Advances in Machine Learning and Interpretable AI", Galway, Ireland.
2020-10-04 Talk at MICCAI 2020 Workshop on "Interpretability of Machine Intelligence in Medical Image Computing", Lima, Peru.
2020-09-18 ECML/PKDD 2020 Tutorial on "Explainable AI: Basics and Extensions", Ghent, Belgium.
2020-08-21 Talk at AI for Good Global Summit 2020, Geneva, Switzerland.
2020-07-18 ICML 2020 Workshop on "XXAI: Extending Explainable AI Beyond Deep Models and Classifiers", Vienna, Austria.
2020-06-15 IEEE CVPR 2020 Workshop on "Efficient Deep Learning for Computer Vision", Seattle, USA.
2020-05-05 IEEE ICASSP 2020 Special Session on "Distributed Machine Learning on Wireless Networks", Barcelona, Spain.
2020-05-04 IEEE ICASSP 2020 Tutorial on "Distributed and Efficient Deep Learning", Barcelona, Spain. [Slides]
2019-11-21 Talk at FUTURAS IN RES Conference, Berlin, Germany.
2019-10-27 ICCV 2019 Workshop on Interpretating and Explaining Visual AI Models, Seoul, Korea. [Slides]
2019-08-28 Cross Domain Conference for Machine Learning and Knowledge Extraction, Canterbury, UK.
2019-08-14 Our book "Explainable AI: Interpreting, Explaining and Visualizing Deep Learning" is available
2019-07-23 EMBC 2019 Tutorial on "Interpretable & Transparent Deep Learning", Berlin, Germany. [Slides1] [Slides2] [Slides3] [Slides4]
2019-07-18 Talk at ICIAM Mini-Symposium on "Theoretical Foundations of Deep Learning" in Valencia, Spain.
2019-06-12 Talk at Artificial Intelligence Methods in Cosmology Workshop in Ascona, Switzerland.
2019-05-14 Talk at 5th Digital Future Science Match in Berlin, Germany.
2019-03-22 Talk at Leopoldina Meeting "Digital Pathology on the Boarder to Molecular Imaging" in Venice, Italy.
2019-01-29 Talk at Applied Machine Learning Days, EPFL, Lausanne, Switzerland.
2019-01-09 Talk at Northern Lights Deep Learning Workshop, Tromsø, Norway.
2018-10-07 ICIP 2018 Tutorial on "Interpretable Deep Learning: Towards Understanding & Explaining Deep Neural Networks" in Athens, Greece. [Slides1] [Slides2] [Slides3] [Slides4]
2018-09-16 MICCAI 2018 Tutorial on "Interpretable Machine Learning" in Granada, Spain. [Slides]
2018-08-27 Talk at IAPR Summer School on Machine and Visual Intelligence in Vico Equense, Italy. [Slides]
2018-06-18 CVPR 2018 Tutorial on "Interpreting and Explaining Deep Models in Computer Vision" in Salt Lake City, USA. [Slides1] [Slides2] [Slides3] [Slides4]
2018-06-18 Presentation of our VQA work at the CVPR'18 VQA Workshop in Salt Lake City, USA.
2018-06-11 Demo at CeBIT 2018 in Hannover, Germany.
2018-06-04 Talk at IoT Week: Machine Learning to Exploit Big Data in Bilbao, Spain.
2018-05-15 Talk at AI for Good Global Summit 2018 in Geneva, Switzerland.
2018-04-24 ITU Workshop on "Impact of AI on ICT Infrastructures" in Xi'an, China.
2018-04-10 Talk at 2nd IML Machine Learning Workshop at CERN, Geneva, Switzerland.
2018-02-20 Talk at HAP Workshop | Big Data Science in Astroparticle Physics in Aachen, Germany.
2018-02-08 Talk at Deep Learning for Computational Biology Workshop in Berlin, Germany.
2018-01-29 ITU Workshop on "Machine Learning for 5G and beyond" in Geneva, Switzerland.
2018-01-25 Talk at Hybrid Talks XXIX »Intelligenz« in Berlin, Germany. [Talk]
2017-12-09 NIPS 2017 Workshop "Interpreting, Explaining and Visualizing Deep Learning - Now what ?" in Long Beach, USA.
2017-11-29 Talk at CoSIP Intense Course on Deep Learning in Berlin, Germany. [Slides] [Video]
2017-09-12 GCPR 2017 Tutorial on "Interpretable Machine Learning" in Basel, Switzerland. [Slides]
2017-08-27 Talk at DTU Summer School on Advanced Topics in Machine Learning in Copenhagen, Denmark. [Slides1] [Slides2] [Slides3] [Slides4]
2017-06-25 Workshop "Deep Learning: Theory, Algorithms, and Applications" in Berlin, Germany. [Video]
2017-06-24 Demo at Lange Nacht der Wissenschaften in Berlin, Germany
2017-03-20 Demo at CeBIT 2017 (hall 6, stand B 36) in Hannover, Germany
2017-03-05 ICASSP 2017 Tutorial on "Methods for Interpreting and Understanding Deep Neural Networks" in New Orleans, USA. [Slides1] [Slides2] [Slides3]
2016-12-09 Presentation at Interpretable ML for Complex Systems NIPS 2016 Workshop in Barcelona, Spain
2016-11-24 ACCV 2016 Workshop on Interpretation and Visualization of Deep Neural Nets in Taipei, Taiwan
2016-09-26 The LRP Toolbox presented at the ICIP Visual Technology Showcase in Phoenix, Arizona
2016-09-06 ICANN 2016 Workshop on Machine Learning and Interpretability in Barcelona, Spain

Research

Interpretable & Trustworthy Deep Learning

			Explaining, Interpreting & Understanding Deep Neural Networks
			With the availability of large databases and recent improvements in deep learning methodology, the performance of AI systems is reaching or even exceeding the human level on an increasing number of complex tasks. However, because of their nested non-linear structure, these highly successful machine learning models are usually applied in a black box manner, i.e., no information is provided about what exactly makes them arrive at their predictions. Since this lack of transparency can be a major drawback, e.g., in medical applications, research towards an explainable AI (XAI) has recently attracted increasing attention. Our work focuses on the development of methods for visualizing, explaining and interpreting deep neural networks and other black box ML models. With Layer-wise Relevance Propagation (LRP) we have proposed a general technique for explaining the classifier's decisions by decomposition, i.e., by measuring the contribution of each input variable to the overall prediction. For instance, LRP shows in the example below that the basis for the AI system's decision, namely the classification of the image as "rooster", is the rooster's red comb and wattle. The LRP algorithm has been successfully applied in many domains and has a profound mathematical basis due to it's close relation to the Taylor decomposition of the neural network function. Software: Keras Explanation Toolbox (software, paper), LRP Python & Caffe Toolbox (software, paper), TensorFlow LRP Wrapper (software), LRP Code for LSTM (software, paper), Dataset-wise XAI (software, paper), Quantus Toolbox (software, paper). Related publications: Highlight Paper: Achtibat et al. 2023, Samek et al. 2021, Lapuschkin et al. 2019, Longo et al. 2024, Kauffmann et al. 2025 Tutorials / Overview Papers: Samek 2023, Holzinger et al. 2022, Samek et al. 2022, Montavon et al. 2018, Samek & Müller 2019, Samek et al. 2018 Methods Papers: Bach et al. 2015, Montavon et al. 2017, Montavon et al. 2019, Arras et al. 2017b, Arras et al. 2019, Binder et al. 2016, Kohlbrenner et al. 2020, Pahde et al. 2023, Weber et al. 2023b, Achtibat et al. 2024, Yolcu et al. 2024 Concept-Level Explanations: Achtibat et al. 2023, Dreyer et al. 2023, Dreyer et al. 2024b, Vielhaben et al. 2024b Explaining Unsupervised Learning: Montavon et al. 2022, Kauffmann et al. 2024, Kauffmann et al. 2025 Explaining Regression Problems: Letzgus et al. 2022 Explaining Time Series Problems: Vielhaben et al. 2024, Mirzavand et al. 2023 Evaluation of Explanations: Samek et al. 2017b, Arras et al. 2019, Arras et al. 2022, Binder et al. 2023, Hedström et al. 2023, Dawoud et al. 2023 Software Papers: Hedström et al. 2022, Anders et al. 2021, Alber et al. 2019, Lapuschkin et al. 2016b Interpretability for Sciences: Sturm et al. 2016, Thomas et al. 2019, Horst et al. 2019, Hägele et al. 2020, Slijepcevic et al. 2022, Hofmann et al. 2022, Mirzavand et al. 2023, Tinauer et al. 2024 Interpretability for Text: Arras et al. 2017, Arras et al. 2017b, Arras et al. 2016, Horn et al. 2017 Interpretability for Image: Lapuschkin et al. 2016, Lapuschkin et al. 2017, Seibold et al. 2020, Bach et al. 2016c, Binder et al. 2016b, Arbabzadah et al. 2016, Yu et al. 2024, Höfler et al. 2024 Interpretability for Video: Anders et al. 2019, Srinivasan et al. 2017 Interpretability for Speech: Frommholz et al. 2023, Becker et al. 2024 Application to Neural Network Pruning: Yeom et al. 2021, Becking et al. 2022, Ede et al. 2022, Hatefi et al. 2024 Interpretability and Causality: Rieckmann et al. 2024, Rieckmann et al. 2022 XAI for Dataset Denoising & Model Improvement: Dreyer et al. 2024, Weber et al. 2023, Anders et al. 2022, Sun et al. 2021, Sun et al. 2022, Pahde et al. 2022, Pahde et al. 2023, Bareeva et al. 2024 Short Papers: Samek et al. 2016b, Montavon et al. 2016, Binder et al. 2016c, Samek et al. 2020, Biecek et al. 2024 Book: Samek et al. (Eds.) 2019, Holzinger et al. (Eds.) 2022 Workshop on "XXAI: Extending Explainable AI Beyond Deep Models and Classifiers" in ICML 2020 Tutorials: GCPR 2017 [Slides], DTU Summer School 2017 [Slides1] [Slides2] [Slides3] [Slides4], ICASSP 2017 [Slides1] [Slides2] [Slides3], IML Workshop 2018 [Slides], CVPR 2018 [Slides1] [Slides2] [Slides3] [Slides4], VISMAC Summer School 2018 [Slides], MICCAI 2018 [Slides], ICIP 2018 [Slides1] [Slides2] [Slides3] [Slides4], EMBC 2019 [Slides1] [Slides2] [Slides3] [Slides4], ICCV 2019 XAI [Slides], ECML/PKDD 2020 [Slides1] [Slides2] [Slides3] [Slides4], CVPR 2021 [Slides], ICML 2021 [Slides1] [Slides2] [Slides3], HCI 2022 [Slides], International DL School 2022 [Slides1] [Slides2] [Slides3], International DL School 2024 [Slides1] [Slides2] [Slides3] Demos:

			Reliable and Robust Deep Learning
			Adversarial attacks on deep learning models have been demonstrated to be imperceptible to a human, while decreasing the model performance considerably. We develop novel strategies that allow to train robust and trustworthy deep neural networks. For instance, to cope with adversarial samples we relax these samples onto the underlying manifold of the (unknown) target class distribution. The adversarial example is transformed back from off-manifold onto the data manifold for which the learning model was originally trained and where it can perform well and robustly. Related publications: Srinivasan et al. 2023, Ruff et al. 2021, Srinivasan et al. 2021, Srinivasan et al. 2019, Laermann et al. 2019, Srinivasan et al. 2020, Oala et al. 2020, Macdonald et al. 2020, Srinivasan et al. 2021, Oala et al. 2021, Berghoff et al. 2021, Oala et al. 2021b, Holzinger et al. 2022, Berghoff et al. 2022, Oala et al. 2023, Oala et al. 2024 Workshop Series on "Trustworthy AI" at AI for Good Global Summit.

Deep Models, Architectures & Applications

			Applications of Deep Learning
			Deep neural networks are an essential component of many state-of-the-art AI systems solving practical problems such as visual object recognition, speech recognition, or natural language processing. We explore the use of deep learning for various classification and recognition tasks on image, text and video data, including image classification, compressed domain object tracking, document topic classification, sentiment analysis, visual question answering, human action recognition and detection of face morphing attacks. Related publications: Lapuschkin et al. 2016, Arbabzadah et al. 2016, Gül et al. 2016, Srinivasan et al. 2017, Lapuschkin et al. 2017, Marban et al. 2017, Arras et al. 2017, Seibold et al. 2017, Bosse et al. 2018, Horst et al. 2019, Bubba et al. 2019, Strodthoff et al. 2020, Hägele et al. 2020, Vielhaben et al. 2020, Slijepcevic et al. 2022

			Recurrent Neural Networks & Deep Architectures
			Recurrent neural networks such as LSTMs have an internal memory which allows to model dynamic temporal behavior. This makes them applicable to time series analysis and regression problems. We investigate the use of different recurrent architectures and combinations of CNNs and LSTMs for applications such as sentiment analysis, tracking or visual question answering. Furthermore, we develop variants of the LRP algorithm to explain and interpret these recurrent models. Related publications: Marban et al. 2017, Arras et al. 2017, Osman & Samek 2019, Marban et al. 2019, Arras et al. 2019

			Visual Question Answering
			Visual question answering (VQA) is an challenging task combining techniques from natural language processing and computer vision. The task typically involves showing an image to a computer and asking a question about that image which the computer must answer. We develop novel attention mechanisms to improve the performance of state-of-the-art VQA models. Related publications: Osman & Samek 2019, Arras et al. 2020 Demo:

			Image Quality Assessment
			Enormous amounts of visual media are ubiquitous today and considerable time and resources are brought up to ensure that the captured, transmitted or presented media is of satisfactory quality. Image quality assessment (IQA) aims at providing methods to measure the quality of visual data in a way that is consistent with human perception. We develop deep CNN models for no-reference and full-reference IQA and investigate the applicability of EEG-based approaches for measuring the perceived quality of images and 3D visualizations. Related publications: Bosse et al. 2016, Bosse et al. 2016b, Bosse et al. 2016c, Bosse et al. 2017, Avarvand et al. 2017, Bosse et al. 2018, Bosse et al. 2018b, Bosse et al. 2018c, Bosse et al. 2019 Special Issue: "Quality Perception of Advanced Multimedia Systems" in Digital Signal Processing

Machine Learning in Communications

Standardization Activities

The International Telecommunication Union (ITU) Focus Group on "Machine Learning for Future Networks including 5G" was established to identify relevant gaps and issues in standardization activities related to the use of machine learning in communications. This includes technical aspects such as use cases, possible requirements, algorithms, architectures and protocols. With our research we are supporting these activities and especially contributing to the working group on "Data Formats & ML Technologies".

The Moving Picture Experts Group (MPEG) aims to define a compressed, interpretable and interoperable representation for trained neural networks. We support these activities with our research on neural network compression and computationally efficient neural network formats.

More information:	Focus Group on "Machine Learning for Future Networks including 5G"
	MPEG Group on "Compression of Neural Networks for Multimedia Content Description and Analysis"

			Machine Learning for 5G Networks
			Our research investigates the use of machine learning methods for improving communication, in particular in 5G networks. For instance, we investigate Early Hybrid Automatic Repeat reQuest feedback schemes enhanced by machine learning techniques as possible path towards ultra-reliable and low-latency communication. Here ML methods can be used to predict the outcome of the decoding process ahead of the end of the transmission. Related publications: Strodthoff et al. 2018b, Strodthoff et al. 2019

			Neural Network Compression and Efficient Deep Learning
			State-of-the-art machine learning models such as deep neural networks are known to work excellently in practice. However, since the training and execution of these models require extensive computational resources, they may not be applicable in communications systems with limited storage capabilities, computational power and energy resources, e.g., smartphones, embedded systems or IoT devices. Our research addresses this problem and focuses on the development of techniques for reducing the complexity and increasing the execution efficiency of deep neural networks. Software: DeepCABAC (software, paper) Related publications: Samek et al. 2018b, Ehmann & Samek 2018, Wiedemann et al. 2019, Wiedemann et al. 2019b, Wiedemann et al. 2020, Wiedemann et al. 2020b, Marban et al. 2020, Wiedemann et al. 2020c, Haase et al. 2020, Wiedemann et al. 2021, Haase et al. 2021, Kirchhoffer et al. 2022, Yeom et al. 2021, Becking et al. 2022, Tech et al. 2022 Workshop on "Efficient Deep Learning for Computer Vision" in CVPR 2020 Special Session on "Neural Network Compression and Compact Deep Features: From Methods to Standards" at IEEE ICIP 2021

			Distributed & Federated Deep Learning
			Large deep neural networks are trained on huge data corpora. Therefore, distributed training schemes are becoming increasingly relevant. A major issue in distributed training is the limited communication bandwidth between contributing nodes or prohibitive communication cost in general. In our research we investigate new methods for reducing the communication cost for distributed training. This includes techniques of communication delay and gradient sparsification as well as optimal weight update encoding. Our results show that the upstream communication can be reduced by more than four orders of magnitude without significantly harming the convergence speed. Software: Robust and Communication-Efficient Federated Learning from Non-IID Data (software, paper), Clustered Federated Learning (software, paper) Related publications: Sattler et al. 2019, Sattler et al. 2019b, Sattler et al. 2020, Sattler et al. 2020b, Sattler et al. 2020c, Neumann et al. 2020,Sattler et al. 2021, Sattler et al. 2023, Sattler et al. 2022b, Witt et al. 2023, Becking et al. 2022, Witt et al. 2023, Hoech et al. 2023, Rischke et al. 2022, Neumann et al. 2023, Becking et al. 2024, Witt et al. 2024, Witt et al. 2024b Special Session on "Distributed Machine Learning on Wireless Networks" in ICASSP 2020 Tutorials: ICASSP 2020 [Slides]

			Machine Learning for Video Communication
			By 2019 video data is estimated to make up to 80% of all uploads and downloads on the internet. The transmission and storage of such huge amount of data was only made possible through the use of steadily improving video compression algorithms. In our research we develop machine learning enhanced video coding schemes as well as compressed domain video analysis algorithms, which operate with the data encoded in compressed video bitstream such as motion vectors, block coding modes or transform coefficients of the motion-compensated prediction residuals. Compressed domain approaches generally have lower computational cost compared to pixel domain approaches since they avoid a full decoding of the video, thereby reducing the amount of processing and storage requirements significantly. Related publications: Gül et al. 2016, Srinivasan et al. 2016, Srinivasan et al. 2017, Pfaff et al. 2018, Vielhaben et al. 2019

Machine Learning for Health

			EEG, ECG and Time Series Signals
			Many biomedical recordings are represented as a time series. For instance, electroencephalography is one of the most popular methods used for the acquisition of neural data. Analysing these signals is challenging, because of its complex spatio-temporal characteristics and low signal-to-noise level. In our research we develop novel techniques for analysis of neural signals as well as other biomedical time series data. Related publications: Samek et al. 2013, Dähne et al. 2015, Sturm et al. 2016, Samek et al. 2016, Avarvand et al. 2017, Samek et al. 2017, Avarvand et al. 2018, Bosse et al. 2018, Horst et al. 2019, Wagner et al. 2020, Sattler et al. 2020, Strodthoff et al. 2021, Slijepcevic et al. 2022

			fMRI, CT and Imaging Data
			The analysis of imaging data poses several strong challenges, in particular, due to its high dimensionality, its strong spatio-temporal correlation, missing measurements and the comparably small sample sizes of these data sets. Our research focuses on the development of deep learning based frameworks that overcomes these challenges. Our LSTM-based model for the analysis and interpretation of whole-brain neuroimaging data scales well to large data sets and is mathematically non-linear, while still maintaining interpretability of the data. It can identify associations between the brain activity of individuals and their underlying cognitive states, not just on the group- or subject-level, but also on the level of single trials and brain samples. Furthermore, we research new algorithms for the analysis of histopathological images and develop hybrid reconstruction frameworks for the inverse problem of limited angle computed tomography, in which entire boundary sections are not captured in the measurements. Our approach fuses model-based sparse regularization with data-driven deep learning. It is reliable in the sense that we only learn the part that can provably not be handled by model-based methods, while applying the theoretically controllable sparse regularization technique to the remaining parts. Related publications: Thomas et al. 2019, Thomas et al. 2019b, Bubba et al. 2019, Wagner et al. 2019, Hägele et al. 2020, Schwendicke et al. 2020, Srinivasan et al. 2022, Hofmann et al. 2022, Thomas et al. 2023, Föllmer et al. 2022, Ma et al. 2022, Rischke et al. 2022, Wagner et al. 2022, Hansmann et al. 2023

			Sequences & Structured Medical Data
			Inferring the properties from sequence data is one of the key problems in bioinformatics. For instance, There is a large body of literature on methods to infer protein properties, most of which make use of additional handcrafted features in addition to the primary sequence alone. We develop novel universal deep sequence models that are pretrained on a language modeling task and can be finetuned on various protein classification tasks. Leveraging large amount of unlabeled data in the form of large, in parts very well-curated protein databases by the use of modern NLP methods, represents a new paradigm in the domain of proteomics. Related publications: Strodthoff et al. 2020, Vielhaben et al. 2020, Rieckmann et al. 2022, Rieckmann et al. 2024

ML Theory & Robust Statistics

			Robustness to Outliers and Nonstationarity
			Parameter estimation is one of the key tasks in statistics, signal processing and machine learning and has a substantial influence on the performance of algorithms in these fields. The robustness of an estimator is of central importance as data are not only noisy but often also contaminated by outliers and nonstationarity. Our research addresses this problem and focuses on the development of robust algorithms and estimators. Related publications: Samek et al. 2012, Samek et al. 2013, Samek et al. 2017

			Robust Subspace Analysis
			Subspace methods are one of the most common basic tools in many research fields, including machine learning, signal processing, image processing, computer vision, natural language processing, e-commerce, and bioinformatics. Since the available data size, dimension and multi-modality has grown explosively, the importance of subspace methods has increased. Our research focuses on the development of subspace methods, which are efficient and robust against outliers and nonstationarity. Related publications: Samek et al. 2013, Kawanabe et al. 2014, Samek et al. 2014, Kaltenstadler et al. 2018

Teaching

			Responsible Artificial Intelligence 1 (Lecture, TU Berlin, Winter 2024/25)
			From Feature Attributions to Next-Generation Explainable AI (Course, 11th International School on Deep Learning, July 2024)
			Responsible Artificial Intelligence 2 (Lecture, TU Berlin, Summer 2024)
			Explainable Artificial Intelligence (Guest Lectures, Warsaw University of Technology, Winter 2023/24)
			Responsible Artificial Intelligence 1 (Lecture, TU Berlin, Winter 2023/24)
			Machine Learning & Communications (Seminar, TU Berlin, Summer 2023)
			Explainable AI: Concepts, Methods and Applications (Course, 6th International Gran Canaria School on Deep Learning, July 2022)
			Toward Explainable AI (Tutorial, HCI International 2022, June 2022)
			Toward Explainable AI (Tutorial, ICML Workshop on XAI, July 2021)
			XXAI: eXtending XAI towards Actionable Interpretability (Tutorial, IEEE CVPR, June 2021)
			Distributed Deep Learning: Concepts, Methods & Applications in Wireless Networks (Tutorial, IEEE GlobeCom, December 2020)
			Machine Learning I (Lecture, TU Berlin, Winter 2020/21)
			Explainable AI for Deep Networks: Basics and Extensions (Tutorial, ECML-PKDD, September 2020)
			Introduction to Explainable AI (Course, International Summer School on Deep Learning, September 2020)
			Interpretable and Explainable Deep Learning (Course, Summer School on Machine Learning in Bioinformatics, August 2020)
			Distributed and Efficient Deep Learning (Tutorial, ICASSP, May 2020)
			Machine Learning II (Lecture, TU Berlin, Summer 2020)
			Interpretable & Transparent Deep Learning (Tutorial, EMBC, July 2019)
			Interpretable Deep Learning: Towards Understanding & Explaining Deep Neural Networks (Tutorial, ICIP, October 2018)
			Interpretable Machine Learning (Tutorial, MICCAI, September 2018)
			Beginners Workshop Machine Learning (Course, TU Berlin, September 2018)
			Interpreting and Explaining Deep Models in Computer Vision (Course, IAPR Summer School on Machine and Visual Intelligence, August 2018)
			Interpreting and Explaining Deep Models in Computer Vision (Tutorial, CVPR, June 2018)
			Interpreting Deep Neural Networks and their Predictions (Tutorial, IML Workshop, CERN, April 2018)
			Cognitive Algorithms (Lecture, TU Berlin, Summer 2018)
			CoSIP Intense Course on Deep Learning (Course, CoSIP Winter School, November 2017)
			Cognitive Algorithms (Lecture, TU Berlin, Winter 2017/18)
			Interpretable Machine Learning (Tutorial, GCPR, September 2017)
			Interpretable Machine Learning (Course, DTU Summer School on Advanced Topics in ML, August 2017)
			Cognitive Algorithms (Lecture, TU Berlin, Summer 2017)
			Methods for Interpreting and Understanding Deep Neural Networks (Tutorial, ICASSP, March 2017)
			Cognitive Algorithms (Lecture, TU Berlin, Winter 2016/17)
			Cognitive Algorithms (Lecture, TU Berlin, Summer 2016)
			Cognitive Algorithms (Lecture, TU Berlin, Winter 2015/16)
			Hot Topics in Machine Learning: Deep Learning (Seminar, TU Berlin, Summer 2015)
			Big Data Course (Practical Course, TU Berlin, Winter 2014/15)
			Classical Topics in Machine Learning (Seminar, TU Berlin, Winter 2014/15)
			Machine Learning for Biomedical Engineering (Seminar, TU Berlin, Summer 2014)

Publications

Preprints

9.	Johanna Vielhaben, Dilyara Bareeva, Jim Berend, Wojciech Samek, Nils Strodthoff: Beyond Scalars: Concept-Based Alignment Analysis in Vision Transformers arXiv:2412.06639, 2024 [bibtex] [pdf] [url]

8.	Andreas Lutz, Gabriele Steidl, Karsten Müller, Wojciech Samek: Optimizing Federated Learning by Entropy-Based Client Selection arXiv:2411.01240, 2024 [bibtex] [pdf] [url]

7.	Galip Ü. Yolcu, Thomas Wiegand, Wojciech Samek, Sebastian Lapuschkin: DualView: Data Attribution from the Dual Perspective arXiv:2402.12118, 2024 [bibtex] [pdf] [url]

6.	Leander Weber, Jim Berend, Alexander Binder, Thomas Wiegand, Wojciech Samek, Sebastian Lapuschkin: Layer-wise Feedback Propagation arXiv:2308.12053, 2023 [bibtex] [pdf] [url]

5.	Frederik Pahde, Leander Weber, Christopher J. Anders, Wojciech Samek, Sebastian Lapuschkin: Navigating Neural Space: Revisiting Concept Activation Vectors to Overcome Directional Divergence arXiv:2202.03482, 2022 [bibtex] [pdf] [url]

4.	Christopher J. Anders, David Neumann, Wojciech Samek, Klaus-Robert Müller, and Sebastian Lapuschkin: Software for Dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAy arXiv:2106.13200, 2021 [bibtex] [pdf] [url]

3.	Kim Nicoli, Pan Kessel, Nils Strodthoff, Wojciech Samek, Klaus-Robert Müller, and Shinichi Nakajima: Comment on "Solving Statistical Mechanics Using VANs": Introducing saVANt - VANs Enhanced by Importance and MCMC Sampling arXiv:1903.11048, 2019 [bibtex] [pdf] [url]

2.	Franziska Horn, Leila Arras, Grégoire Montavon, Klaus-Robert Müller, and Wojciech Samek: Discovering Topics in Text Datasets by Visualizing Relevant Words arXiv:1707.06100, 2017 [bibtex] [pdf] [url]

1.	Franziska Horn, Leila Arras, Grégoire Montavon, Klaus-Robert Müller, and Wojciech Samek: Exploring Text Datasets by Visualizing Relevant Words arXiv:1707.05261, 2017 [bibtex] [pdf] [url]

Whitepapers

Christian Berghoff, Jona Böddinghaus, Vasilios Danos, Gabrielle Davelaar, Thomas Doms, Heiko Ehrich, Alexandru Forrai, Radu Grosu, Ronan Hamon, Henrik Junklewitz, Matthias Neu, Simon Romanski, Wojciech Samek, Dirk Schlesinger, Jan-Eve Stavesand, Sebastian Steinbach, Arndt von Twickel, Robert Walter, Johannes Weissenböck, Markus Wenzel, and Thomas Wiegand:
Towards Auditable AI Systems: From Principles to Practice
BSI, VdTÜV and Fraunhofer HHI, 1-26, 2022
[bibtex] [pdf]

Christian Berghoff, Battista Biggio, Elisa Brummel, Vasilios Danos, Thomas Doms, Heiko Ehrich, Thorsten Gantevoort, Barbara Hammer, Joachim Iden, Sven Jacob, Heidy Khlaaf, Lars Komrowski, Robert Kröwing, Jan Hendrik Metzen, Matthias Neu, Fabian Petsch, Maximilian Poretschkin, Wojciech Samek, Hendrik Schäbe, Arndt von Twickel, Martin Vechev, and Thomas Wiegand:
Towards Auditable AI Systems: Current Status and Future Directions
BSI, VdTÜV and Fraunhofer HHI, 1-32, 2021
[bibtex] [pdf]

Anko Börner, Heinz-Wilhelm Hübers, Odej Kao, Florian Schmidt, Sören Becker, Joachim Denzler, Daniel Matolin, David Haber, Sergio Lucia, Wojciech Samek, Rudolph Triebel, Sascha Eichstädt, Felix Biessmann, Anna Kruspe, Peter Jung, Manon Kok, Guillermo Gallego, Ralf Berger:
Sensor Artificial Intelligence and its Application to Space Systems -- A White Paper
arXiv:2006.08368, 2020
[bibtex] [pdf] [url]

Edited Books & Special Issues

4.	Andreas Holzinger, Randy Goebel, Ruth Fong, Taesup Moon, Klaus-Robert Müller, and Wojciech Samek (Eds.): xxAI - Beyond Explainable AI Lecture Notes in Artificial Intelligence, Springer, 13200:1-397, 2022 [bibtex] [url]

3.	Wojciech Samek, Grégoire Montavon, Andrea Vedaldi, Lars Kai Hansen, and Klaus-Robert Müller (Eds.): Explainable AI: Interpreting, Explaining and Visualizing Deep Learning Lecture Notes in Computer Science, Springer, 11700:1-439, 2019 [bibtex] [url]

2.	Sergio Cruces, Rubén Martín-Clemente, and Wojciech Samek (Eds.): Information Theory Applications in Signal Processing Entropy, 21(7):653, 2019 [bibtex] [url]

1.	Guangtao Zhai, Ke Gu, Jiheng Wang, and Wojciech Samek (Eds.): Quality Perception of Advanced Multimedia Systems Digital Signal Processing, 91:1-104, 2019 [bibtex] [url]

Book Chapters

13.	Ximeng Cheng, Marc Vischer, Zachary Schellin, Leila Arras, Monique M. Kuglitsch, Wojciech Samek, Jackie Ma: Explainability in GeoAI Handbook of Geospatial Artificial Intelligence, CRC Press, 177-201, 2023 [bibtex] [pdf] [url]

12.	Wojciech Samek: Explainable Deep Learning: Methods, Concepts and New Developments Explainable Deep Learning AI: Methods and Challenges, Academic Press, 7-33, 2023 [bibtex] [pdf] [url]

11.	Daniel Becking, Maximilian Dreyer, Wojciech Samek, Karsten Müller, Sebastian Lapuschkin: ECQx: Explainability-Driven Quantization for Low-Bit and Sparse DNNs xxAI - Beyond Explainable AI, LNAI, Springer, 13200:271-296, 2022 [bibtex] [pdf] [url]

10.	Grégoire Montavon, Jacob Kauffmann, Wojciech Samek, and Klaus-Robert Müller: Explaining the Predictions of Unsupervised Learning Models xxAI - Beyond Explainable AI, LNAI, Springer, 13200:117-138, 2022 [bibtex] [pdf] [url]

9.	Andreas Holzinger, Anna Saranti, Christoph Molnar, Przemyslaw Biece, and Wojciech Samek: Explainable AI Methods - A Brief Overview xxAI - Beyond Explainable AI, LNAI, Springer, 13200:13-38, 2022 [bibtex] [pdf] [url]

8.	Wojciech Samek, Leila Arras, Ahmed Osman, Grégoire Montavon, and Klaus-Robert Müller: Explaining the Decisions of Convolutional and Recurrent Neural Networks Mathematical Aspects of Deep Learning, Cambridge University Press, 229–266, 2022 [bibtex] [pdf] [url]

7.	Grégoire Montavon and Wojciech Samek: Explaining the Decisions of Deep Neural Networks and Beyond Statistics meets Machine Learning, Report No. 4/2020, Mathematisches Forschungsinstitut Oberwolfach, pp. 5-8, 2020 [bibtex] [pdf] [url]

6.	Wojciech Samek and Klaus-Robert Müller: Towards Explainable Artificial Intelligence Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, LNCS, Springer, 11700:5-22, 2019 [bibtex] [pdf] [url]

Leila Arras, José Arjona-Medina, Michael Widrich, Grégoire Montavon, Michael Gillhofer, Klaus-Robert Müller, Sepp Hochreiter, and Wojciech Samek:
Explaining and Interpreting LSTMs
Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, LNCS, Springer, 11700:211-238, 2019
[bibtex] [pdf] [url]

4.	Grégoire Montavon, Alexander Binder, Sebastian Lapuschkin, Wojciech Samek, and Klaus-Robert Müller: Layer-Wise Relevance Propagation: An Overview Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, LNCS, Springer, 11700:193-209, 2019 [bibtex] [pdf] [url]

3.	Christopher J. Anders, Grégoire Montavon, Wojciech Samek, and Klaus-Robert Müller: Understanding Patch-Based Learning of Video Data by Explaining Predictions Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, LNCS, Springer, 11700:297-309, 2019 [bibtex] [pdf] [url]

2.	Wojciech Samek: Über die robuste räumliche Filterung von EEG in nichtstationären Umgebungen Ausgezeichnete Informatikdissertationen 2014, GI-Edition - Lecture Notes in Informatics (LNI), 15:251-60, 2015 [bibtex] [pdf]

1.	Alexander Binder, Wojciech Samek, Klaus-Robert Müller, and Motoaki Kawanabe: Machine Learning for Visual Concept Recognition and Ranking for Images Towards the Internet of Services: The THESEUS Program, Springer-Verlag, 211-23, 2014 [bibtex] [pdf] [url]

Publications in Journals

100.	Jacob Kauffmann, Jonas Dippel, Lukas Ruff, Wojciech Samek, Klaus-Robert Müller, Grégoire Montavon: The Clever Hans Effect in Unsupervised Learning Nature Machine Intelligence, 2025 [bibtex] [pdf] [url]

99.	Florian Bley, Sebastian Lapuschkin, Wojciech Samek, Grégoire Montavon: Explaining Predictive Uncertainty by Exposing Second-Order Effects Pattern Recognition, 160:111171, 2025 [bibtex] [pdf] [url]

98.

Bernhard Föllmer, Sotirios Tsogias, Federico Biavati, Kenrick Schulze, Maria Bosserdt, Lars Gerrit Hövermann, Sebastian Stober, Wojciech Samek, Klaus F. Kofoed, Pál Maurovich-Horvat, Patrick Donnelly, Theodora Benedek, Michelle C. Williams, Marc Dewey:
Automated Segment-Level Coronary Artery Calcium Scoring on Non-Contrast CT: A Multi-Task Deep-Learning Approach
Insights into Imaging, 15:250, 2024
[bibtex] [pdf] [url]

97.

Luis Oala, Manil Maskey, Lilith Bat-Leah, Alicia Parrish, Nezihe Merve Gürel, Tzu-Sheng Kuo, Yang Liu, Rotem Dror, Danilo Brajovic, Xiaozhe Yao, Max Bartolo, William A Gaviria Rojas, Ryan Hileman, Rainier Aliment, Michael W. Mahoney, Meg Risdal, Matthew Lease, Wojciech Samek, Debojyoti Dutta, Curtis G Northcutt, Cody Coleman, Braden Hancock, Bernard Koch, Girmaw Abebe Tadesse, Bojan Karlaš, Ahmed Alaa, Adji Bousso Dieng, Natasha Noy, Vijay Janapa Reddi, James Zou, Praveen Paritosh, Mihaela van der Schaar, Kurt Bollacker, Lora Aroyo, Ce Zhang, Joaquin Vanschoren, Isabelle Guyon, Peter Mattson:
DMLR: Data-centric Machine Learning Research -- Past, Present and Future
Journal of Data-centric Machine Learning Research, 2024
[bibtex] [pdf] [url]

96.

Matthias I Gröschel, Francy J. Pérez-Llanos, Roland Diel, Roger Vargas Jr, Vincent Escuyer, Kimberlee Musser, Lisa Trieu, Jeanne Sullivan Meissner, Jillian Knorr, Don Klinkenberg, Peter Kouw, Susanne Homolka, Wojciech Samek, Barun Mathema, Dick van Soolingen, Stefan Niemann, Shama Ahuja, Maha R Farhat:
Differential Rates of Mycobacterium Tuberculosis Transmission Associate with Host–Pathogen Sympatry
Nature Microbiology, 9:2113–2127, 2024
[bibtex] [pdf] [url]

95.

Luca Longo, Mario Brcic, Federico Cabitza, Jaesik Choi, Roberto Confalonieri, Javier Del Ser, Riccardo Guidotti, Yoichi Hayashi, Francisco Herrera, Andreas Holzinger, Richard Jiang, Hassan Khosravi, Freddy Lecue, Gianclaudio Malgieri, Andrés Páez, Wojciech Samek, Johannes Schneider, Timo Speith, Simone Stumpf:
Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions
Information Fusion, 106:102301, 2024
[bibtex] [pdf] [url]

94.	Jacob Kauffmann, Malte Esders, Grégoire Montavon, Wojciech Samek, and Klaus-Robert Müller: From Clustering to Cluster Explanations via Neural Networks IEEE Transactions on Neural Networks and Learning Systems, 35(2):1926-1940, 2024 [bibtex] [pdf] [supplement] [url]

93.	Johanna Vielhaben, Sebastian Lapuschkin, Grégoire Montavon, Wojciech Samek: Explainable AI for Time Series via Virtual Inspection Layers Pattern Recognition, 150:110309, 2024 [bibtex] [pdf] [url]

92.

Andreas Rieckmann, Sebastian Nielsen, Piotr Dworzynski, Heresh Amini, Søren Wengel Mogensen, Isaquel Bartolomeu Silva, Angela Yuwen Chang, Onyebuchi Aniweta Arah, Wojciech Samek, Naja Hulvej Rod, Claus Thorn Ekstrøm, Christine Stabell Benn, Peter Aaby, Ane Bærent Fisker:
Discovering Sub-Groups of Children with High Mortality in Urban Guinea-Bissau : An Exploratory and Validation Cohort Study
JMIR Public Health and Surveillance, 10:e48060, 2024
[bibtex] [pdf] [url]

91.

Daniel Becking, Karsten Müller, Paul Haase, Heiner Kirchhoffer, Gerhard Tech, Wojciech Samek, Heiko Schwarz, Detlev Marpe, Thomas Wiegand:
Neural Network Coding of Difference Updates for Efficient Distributed Learning Communication
IEEE Transactions on Multimedia, 26:6848-6863, 2024
[bibtex] [pdf] [url]

90.	Sören Becker, Johanna Vielhaben, Marcel Ackermann, Klaus-Robert Müller, Sebastian Lapuschkin and Wojciech Samek: AudioMNIST: Exploring Explainable Artificial Intelligence for Audio Analysis on a Simple Benchmark Journal of the Franklin Institute, 361(1):418-428, 2024 [bibtex] [pdf] [url]

89.	Leon Witt, Usama Zafar, KuoYeh Shen, Felix Sattler, Dan Li, Wojciech Samek: Decentralized and Incentivized Federated Learning: A Blockchain-Enabled Framework Utilising Compressed Soft-Labels and Peer Consistency IEEE Transactions on Services Computing, 17(4):1449-1464, 2024 [bibtex] [pdf] [url]

88.	David Neumann, Andreas Lutz, Karsten Müller, Wojciech Samek: A Privacy Preserving System for Movie Recommendations using Federated Learning ACM Transactions on Recommender Systems, 2023 [bibtex] [pdf] [url]

87.	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 Nature Machine Intelligence, 5:1006–1019, 2023 [bibtex] [pdf] [url]

86.	Felix Sattler, Tim Korjakow, Roman Rischke, and Wojciech Samek: FedAUX: Leveraging Unlabeled Auxiliary Data in Federated Learning IEEE Transactions on Neural Networks and Learning Systems, 34(9):5531-5543, 2023 [bibtex] [pdf] [url]

85.	Armin W. Thomas, Ulman Lindenberger, Wojciech Samek, Klaus-Robert Müller: Evaluating deep transfer learning for whole-brain cognitive decoding Journal of the Franklin Institute, 360(13):9754-9787, 2023 [bibtex] [pdf] [url]

84.	Sara Mirzavand Borujeni, Leila Arras, Vignesh Srinivasan, Wojciech Samek: Explainable Sequence-to-Sequence GRU Neural Network for Pollution Forecasting Scientifc Reports, 13:9940, 2023 [bibtex] [pdf] [url]

83.	Anna Hedström, Philine Bommer, Kristoffer K. Wickstrøm, Wojciech Samek, Sebastian Lapuschkin, Marina M.-C. Höhne: The Meta-Evaluation Problem in Explainable AI: Identifying Reliable Estimators with MetaQuantus Transactions on Machine Learning Research, 2023 [bibtex] [pdf] [url]

82.

Luis Oala, Marco Aversa, Gabriel Nobis, Kurt Willis, Yoan Neuenschwander, Michèle Buck, Christian Matek, Jerome Extermann, Enrico Pomarico, Wojciech Samek, Roderick Murray-Smith, Christoph Clausen, Bruno Sanguinetti:
Data Models for Dataset Drift Controls in Machine Learning With Optical Images
Transactions on Machine Learning Research, 2023
[bibtex] [pdf] [url]

81.

Martin-Leo Hansmann, Frederick Klauschen, Wojciech Samek, Klaus-Robert Müller, Emmanuel Donnadieu, Sonja Scharf, Sylvia Hartmann, Ina Koch, Jörg Ackermann, Liron Pantanowitz, Hendrik Schäfer, Patrick Wurzel:
Imaging bridges pathology and radiology?
Journal of Pathology Informatics, 14:100298, 2023
[bibtex] [pdf] [url]

80.	Leon Witt, Mathis Heyer, Kentaroh Toyoda, Wojciech Samek, and Dan Li: Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review IEEE Internet of Things Journal, 10(4):3642-3663, 2023 [bibtex] [pdf] [url]

79.	Leander Weber, Sebastian Lapuschkin, Alexander Binder, Wojciech Samek: Beyond Explaining: Opportunities and Challenges of XAI-Based Model Improvement Information Fusion, 92:154-176, 2023 [bibtex] [pdf] [url]

78.	Wojciech Samek: Von der Blackbox zur erklärbaren und vertrauenswürdigen KI Quintessenz Zahnmedizin, 73(9):868-873, 2022 [bibtex] [url]

77.	Vignesh Srinivasan, Nils Strodthoff, Jackie Ma, Alexander Binder, Klaus-Robert Müller, and Wojciech Samek: To Pretrain or Not? A Systematic Analysis of the Benefits of Pretraining in Diabetic Retinopathy PLoS ONE, 17(10):e0274291, 2022 [bibtex] [pdf] [url]

76.

Patrick Wagner, Nils Strodthoff, Patrick Wurzel, Arturo Marban, Sonja Scharf, Hendrik Schäfer, Philipp Seegerer, Andreas Loth, Sylvia Hartmann, Frederick Klauschen, Klaus-Robert Müller, Wojciech Samek, and Martin-Leo Hansmann:
New Definitions of Human Lymphoid and Follicular Cell Entities in Lymphatic Tissue by Machine Learning
Scientific Reports, 12:18991, 2022
[bibtex] [pdf] [url]

75.

Simon M. Hofmann, Frauke Beyer, Sebastian Lapuschkin, Ole Goltermann, Markus Loeffler, Klaus-Robert Müller, Arno Villringer, Wojciech Samek, A. Veronica Witte:
Towards the Interpretability of Deep Learning Models for Multi-Modal Neuroimaging: Finding Structural Changes of the Ageing Brain
NeuroImage, 261:119504, 2022
[bibtex] [pdf] [url]

74.	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 Explanation Journal of Machine Learning Research, 24(34):1-11, 2023 [bibtex] [pdf] [url]

73.	Roman Rischke, Lisa Schneider, Karsten Müller, Wojciech Samek, Falk Schwendicke, and Joachim Krois: Federated Learning in Dentistry: Chances and Challenges Journal of Dental Research, 101(11):1269-1273, 2022 [bibtex] [pdf] [url]

72.	Jackie Ma, Lisa Schneider, Sebastian Lapuschkin, Reduan Achtibat, Martha Duchrau, Joachim Krois, Falk Schwendicke, and Wojciech Samek: Towards Trustworthy AI in Dentistry Journal of Dental Research, 101(11):1263-1268, 2022 [bibtex] [pdf] [url]

71.	Bernhard Föllmer, Federico Biavati, Christian Wald, Sebastian Stober, Marc Dewey, Jackie Ma, Wojciech Samek: Active Multi-Task Learning with Uncertainty Weighted Loss for Coronary Calcium Scoring Medical Physics, 49(11):7262-7277, 2022 [bibtex] [pdf] [url]

70.

Saul Calderon-Ramirez, Luis Oala, Jordina Torrents-Barrena, Shengxiang Yang, Armaghan Moemeni, Wojciech Samek, and Miguel A. Molina-Cabello:
Dataset Similarity to Assess Semi-supervised Learning Under Distribution Mismatch Between the Labelled and Unlabelled Datasets
IEEE Transactions on Artificial Intelligence, 4(2):282-291, 2023
[bibtex] [pdf] [url]

69.

Andreas Rieckmann, Piotr Dworzynski, Leila Arras, Sebastian Lapuschkin, Wojciech Samek, Onyebuchi A. Arah, Naja H. Rod, Claus T. Ekstrøm:
Causes of Outcome Learning: A causal inference-inspired machine learning approach to disentangling common combinations of potential causes of a health outcome
International Journal of Epidemiology, 51(5):1622-1636, 2022
[bibtex] [pdf] [url]

68.	Simon Letzgus, Patrick Wagner, Jonas Lederer, Wojciech Samek, Klaus-Robert Müller, Grégoire Montavon: Toward Explainable AI for Regression Models: A Methodological Perspective IEEE Signal Processing Magazine, 39(4):40-58, 2022 [bibtex] [pdf] [url]

67.	Vignesh Srinivasan, Klaus-Robert Müller, Wojciech Samek, and Shinichi Nakajima: Langevin Cooling for Unsupervised Domain Translation IEEE Transactions on Neural Networks and Learning Systems, 34(10):7675-7688, 2023 [bibtex] [pdf] [url]

66.

Andreas Holzinger, Matthias Dehmer, Frank Emmert-Streib, Rita Cucchiara, Isabelle Augenstein, Javier Del Ser, Wojciech Samek, Igor Jurisica, Natalia Díaz-Rodríguez:
Information Fusion as an Integrative Cross-Cutting Enabler to Achieve Robust, Explainable, and Trustworthy Medical Artificial Intelligence
Information Fusion, 79:263-278, 2022
[bibtex] [pdf] [url]

65.	Leila Arras, Ahmed Osman, and Wojciech Samek: CLEVR-XAI: A Benchmark Dataset for the Ground Truth Evaluation of Neural Network Explanations Information Fusion, 81:14-40, 2022 [bibtex] [pdf] [url]

64.	Jiamei Sun, Sebastian Lapuschkin, Wojciech Samek, and Alexander Binder: Explain and Improve: LRP-Inference Fine Tuning for Image Captioning Models Information Fusion, 77:233-246, 2022 [bibtex] [pdf] [url]

63.	Christopher J. Anders, Leander Weber, David Neumann, Wojciech Samek, Klaus-Robert Müller, and Sebastian Lapuschkin: Finding and Removing Clever Hans: Using Explanation Methods to Debug and Improve Deep Models Information Fusion, 77:261-295, 2022 [bibtex] [pdf] [url]

62.

Heiner Kirchhoffer, Paul Haase, Wojciech Samek, Karsten Müller, Hamed Rezazadegan-Tavakoli, Francesco Cricri, Emre Aksu, Miska M. Hannuksela, Wei Jiang, Wei Wang, Shan Liu, Swayambhoo Jain, Shahab Hamidi-Rad, Fabien Racapé, and Werner Bailer:
Overview of the Neural Network Compression and Representation (NNR) Standard
IEEE Transactions on Circuits and Systems for Video Technology, 32(5):3203-3216, 2022
[bibtex] [pdf] [url]

61.

Djordje Slijepcevic, Fabian Horst, Brian Horsak, Sebastian Lapuschkin, Anna-Maria Raberger, Andreas Kranzl, Wojciech Samek, Christian Breiteneder, Wolfgang I. Schöllhorn, and Matthias Zeppelzauer:
Explaining Machine Learning Models for Clinical Gait Analysis
ACM Transactions on Computing for Healthcare, 3(2):1-27, 2022
[bibtex] [pdf] [url] [code]

60.	Karsten Müller, Wojciech Samek, and Detlev Marpe: Ein internationaler KI-Standard zur Kompression Neuronaler Netze FKT- Fachzeitschrift für Fernsehen, Film und Elektronische Medien, 33-36, 2021 [bibtex] [pdf] [url]

59.

Luis Oala, Andrew G. Murchison, Pradeep Balachandran, Shruti Choudhary, Jana Fehr, Alixandro Werneck Leite, Peter G. Goldschmidt, Christian Johner, Elora D. M. Schörverth, Rose Nakasi, Martin Meyer, Federico Cabitza, Pat Baird, Carolin Prabhu, Eva Weicken, Xiaoxuan Liu, Markus Wenzel, Steffen Vogler, Darlington Akogo, Shada Alsalamah, Emre Kazim, Adriano Koshiyama, Sven Piechottka, Sheena Macpherson, Ian Shadforth, Regina Geierhofer, Christian Matek, Joachim Krois, Bruno Sanguinetti, Matthew Arentz, Pavol Bielik, Saul Calderon-Ramirez, Auss Abbood, Nicolas Langer, Stefan Haufe, Ferath Kherif, Sameer Pujari, Wojciech Samek, and Thomas Wiegand:
Machine Learning for Health: Algorithm Auditing & Quality Control
Journal of Medical Systems, 45:105, 2021
[bibtex] [pdf] [url]

58.	Luis Oala, Cosmas Heiß, Jan Macdonald, Maximilian März, Gitta Kutyniok, and Wojciech Samek: Detecting Failure Modes in Image Reconstructions with Interval Neural Network Uncertainty International Journal of Computer Assisted Radiology and Surgery, 16:2089–2097, 2021 [bibtex] [pdf] [url]

57.

Simon Wiedemanny, Suhas Shivapakashy, Pablo Wiedemanny, Daniel Becking, Wojciech Samek, Friedel Gerfers, and Thomas Wiegand:
FantastIC4: A Hardware-Software Co-Design Approach for Efficiently Running 4bit-Compact Multilayer Perceptrons
IEEE Open Journal of Circuits and Systems, 2:407-419, 2021
[bibtex] [pdf] [url]

56.	Felix Sattler, Arturo Marban, Roman Rischke, Wojciech Samek: CFD: Communication-Efficient Federated Distillation via Soft-Label Quantization and Delta Coding IEEE Transactions on Network Science and Engineering, 9(4):2025-2038, 2022 [bibtex] [pdf] [url]

55.	Wojciech Samek, Grégoire Montavon, Sebastian Lapuschkin, Christopher J. Anders, and Klaus-Robert Müller: Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications Proceedings of the IEEE, 109(3):247-278, 2021 [bibtex] [pdf] [url]

54.	Seul-Ki Yeom, Philipp Seegerer, Sebastian Lapuschkin, Alexander Binder, Simon Wiedemann, Klaus-Robert Müller, and Wojciech Samek: Pruning by Explaining: A Novel Criterion for Deep Neural Network Pruning Pattern Recognition, 115:107899, 2021 [bibtex] [pdf] [url]

53.	Lukas Ruff, Jacob R. Kauffmann, Robert A. Vandermeulen, Grégoire Montavon, Wojciech Samek, Marius Kloft, Thomas G. Dietterich, and Klaus-Robert Müller: A Unifying Review of Deep and Shallow Anomaly Detection Proceedings of the IEEE, 109(5):756-795, 2021 [bibtex] [pdf] [url]

52.	Vignesh Srinivasan, Arturo Marban, Klaus-Robert Müller, Wojciech Samek, and Shinichi Nakajima: Robustifying Models Against Adversarial Attacks by Langevin Dynamics Neural Networks, 137:1-17, 2021 [bibtex] [pdf] [url]

51.	Nils Strodthoff, Patrick Wagner, Tobias Schaeffter, and Wojciech Samek: Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL IEEE Journal of Biomedical And Health Informatics, 25(5):1519-1528, 2021 [bibtex] [pdf] [url]

50.	Felix Sattler, Klaus-Robert Müller, and Wojciech Samek: Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints IEEE Transactions on Neural Networks and Learning Systems, 32(8):3710-3722, 2021 [bibtex] [pdf] [supplement] [url] [code]

49.	Felix Sattler, Jackie Ma, Patrick Wagner, David Neumann, Markus Wenzel, Ralf Schäfer, Wojciech Samek, Klaus-Robert Müller, and Thomas Wiegand: Risk Estimation of SARS-CoV-2 Transmission from Bluetooth Low Energy Measurements Nature Digital Medicine, 3:129, 2020 [bibtex] [pdf] [url]

48.	Johanna Vielhaben, Markus Wenzel, Wojciech Samek, and Nils Strodthoff: USMPep: Universal Sequence Models for Major Histocompatibility Complex Binding Affinity Prediction BMC Bioinformatics, 21:279, 2020 [bibtex] [pdf] [url] [code]

47.	Clemens Seibold, Wojciech Samek, Anna Hilsmann, and Peter Eisert: Accurate and Robust Neural Networks for Face Morphing Attack Detection Journal of Information Security and Applications, 53:102526, 2020 [bibtex] [pdf] [url]

46.	Patrick Wagner, Nils Strodthoff, Ralf-Dieter Bousseljot, Dieter Kreiseler, Fatima I. Lunze, Wojciech Samek, and Tobias Schaeffter: PTB-XL, A Large Publicly Available Electrocardiography Dataset Scientific Data, 7:154, 2020 [bibtex] [pdf] [url]

45.

Miriam Hägele, Philipp Seegerer, Sebastian Lapuschkin, Michael Bockmayr, Wojciech Samek, Frederick Klauschen, Klaus-Robert Müller, and Alexander Binder:
Resolving Challenges in Deep Learning-Based Analyses of Histopathological Images using Explanation Methods
Scientific Reports, 10:6423, 2020
[bibtex] [pdf] [url]

44.	Falk Schwendicke, Wojciech Samek, and Joachim Krois: Artificial Intelligence in Dentistry: Chances and Challenges Journal of Dental Research, 99(7):769-774, 2020 [bibtex] [pdf] [url]

43.	Felix Sattler, Thomas Wiegand and Wojciech Samek: Trends and Advancements in Deep Neural Network Communication ITU Journal: ICT Discoveries, 3(1):53-63, 2020 [bibtex] [pdf] [url]

42.	Kim A. Nicoli, Shinichi Nakajima, Nils Strodthoff, Wojciech Samek, Klaus-Robert Müller, and Pan Kessel: Asymptotically Unbiased Estimation of Physical Observables with Neural Samplers Physical Review E, 101(2):023304, 2020 [bibtex] [pdf] [url]

41.

Simon Wiedemann, Heiner Kirchhoffer, Stefan Matlage, Paul Haase, Arturo Marban, Talmaj Marinc, David Neumann, Tung Nguyen, Ahmed Osman, Heiko Schwarz, Detlev Marpe, Thomas Wiegand, and Wojciech Samek:
DeepCABAC: A Universal Compression Algorithm for Deep Neural Networks
IEEE Journal of Selected Topics in Signal Processing, 14(4):700-714, 2020
[bibtex] [pdf] [url] [code]

40.	Nils Strodthoff, Patrick Wagner, Markus Wenzel, and Wojciech Samek: UDSMProt: Universal Deep Sequence Models for Protein Classification Bioinformatics, 36(8):2401-2409, 2020 [bibtex] [pdf] [url] [code]

39.	Wojciech Samek: Learning with Explainable Trees Nature Machine Intelligence, 2:16-17, 2020 [bibtex] [pdf] [url]

38.	Simon Wiedemann, Klaus-Robert Müller, and Wojciech Samek: Compact and Computationally Efficient Representation of Deep Neural Networks IEEE Transactions on Neural Networks and Learning Systems, 31(3):772-785, 2020 [bibtex] [pdf] [url]

37.	Felix Sattler, Simon Wiedemann, Klaus-Robert Müller, and Wojciech Samek: Robust and Communication-Efficient Federated Learning from Non-IID Data IEEE Transactions on Neural Networks and Learning Systems, 31(9):3400-3413, 2020 [bibtex] [pdf] [url] [code]

36.	Armin W. Thomas, Hauke R. Heekeren, Klaus-Robert Müller, and Wojciech Samek: Analyzing Neuroimaging Data Through Recurrent Deep Learning Models Frontiers in Neuroscience, 13:1321, 2019 [bibtex] [pdf] [url]

35.	Nils Strodthoff, Baris Göktepe, Thomas Schierl, Cornelius Hellge, and Wojciech Samek: Enhanced Machine Learning Techniques for Early HARQ Feedback Prediction in 5G IEEE Journal on Selected Areas in Communications, 37(11):2573-2587, 2019 [bibtex] [pdf] [url]

34.	Ahmed Osman and Wojciech Samek: DRAU: Dual Recurrent Attention Units for Visual Question Answering Computer Vision and Image Understanding, 185:24-30, 2019 [bibtex] [pdf] [supplement] [url]

33.	Sebastian Lapuschkin, Stephan Wäldchen, Alexander Binder, Grégoire Montavon, Wojciech Samek, and Klaus-Robert Müller: Unmasking Clever Hans Predictors and Assessing What Machines Really Learn Nature Communications, 10:1096, 2019 [bibtex] [pdf] [url]

32.	Tatiana A. Bubba, Gitta Kutyniok, Matti Lassas, Maximilian März, Wojciech Samek, Samuli Siltanen, and Vignesh Srinivasan: Learning The Invisible: A Hybrid Deep Learning-Shearlet Framework for Limited Angle Computed Tomography Inverse Problems, 35(6):064002, 2019 [bibtex] [pdf] [url]

31.	Fabian Horst, Sebastian Lapuschkin, Wojciech Samek, Klaus-Robert Müller, and Wolfgang I. Schöllhorn: Explaining the Unique Nature of Individual Gait Patterns with Deep Learning Scientific Reports, 9:2391, 2019 [bibtex] [pdf] [supplement] [url]

30.	Maximilian Alber, Sebastian Lapuschkin, Philipp Seegerer, Miriam Hägele, Kristof T. Schütt, Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller, Sven Dähne, and Pieter-Jan Kindermans: iNNvestigate neural networks! Journal of Machine Learning Research, 20(93):1-8, 2019 [bibtex] [pdf] [url] [code]

29.	Arturo Marban, Vignesh Srinivasan, Wojciech Samek, Josep Fernández, and Alicia Casals: A Recurrent Convolutional Neural Network Approach for Sensorless Force Estimation in Robotic Surgery Biomedical Signal Processing and Control, 50:134-150, 2019 [bibtex] [pdf] [url]

28.	Sebastian Bosse, Sören Becker, Klaus-Robert Müller, Wojciech Samek, and Thomas Wiegand: Estimation of Distortion Sensitivity for Visual Quality Prediction Using a Convolutional Neural Network Digital Signal Processing, 91:54-65, 2019 [bibtex] [pdf] [url]

27.	Stephan Kaltenstadler, Shinichi Nakajima, Klaus-Robert Müller, and Wojciech Samek: Wasserstein Stationary Subspace Analysis IEEE Journal of Selected Topics in Signal Processing, 12(6):1213-1223, 2018 [bibtex] [pdf] [url]

26.

Sebastian Bosse, Laura Acqualagna, Wojciech Samek, Anne K. Porbadnigk, Gabriel Curio, Benjamin Blankertz, Klaus-Robert Müller, and Thomas Wiegand:
Assessing Perceived Image Quality Using Steady-State Visual Evoked Potentials and Spatio-Spectral Decomposition
IEEE Transactions on Circuits and Systems for Video Technology, 28(8):1694-1706, 2018
[bibtex] [pdf] [url]

25.	Wiktor Pronobis, Danny Panknin, Johannes Kirschnick, Vignesh Srinivasan, Wojciech Samek, Volker Markl, Manohar Kaul, Klaus-Robert Müller, and Shinichi Nakajima: Sharing Hash Codes for Multiple Purposes Japanese Journal of Statistics and Data Science, 1(1):215246, 2018 [bibtex] [pdf] [url]

24.	Forooz Shahbazi Avarvand, Sarah Bartz, Christina Andreou, Wojciech Samek, Gregor Leicht, Christoph Mulert, Andreas K. Engel, and Guido Nolte: Localizing Bicoherence from EEG and MEG NeuroImage, 174:352-363, 2018 [bibtex] [pdf] [url]

23.	Sebastian Bosse, Dominique Maniry, Klaus-Robert Müller, Thomas Wiegand, and Wojciech Samek: Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment IEEE Transactions on Image Processing, 27(1):206-219, 2018 [bibtex] [pdf] [url]

22.	Grégoire Montavon, Wojciech Samek, and Klaus-Robert Müller: Methods for Interpreting and Understanding Deep Neural Networks Digital Signal Processing, 73:1-15, 2018 [bibtex] [pdf] [url]

21.

Wojciech Samek, Thomas Wiegand, and Klaus-Robert Müller:
Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models
ITU Journal: ICT Discoveries - Special Issue 1 - The Impact of Artificial Intelligence (AI) on Communication Networks and Services, 1(1):39-48, 2018
[bibtex] [pdf] [url]

20.	Wojciech Samek, Slawomir Stanczak, and Thomas Wiegand: The Convergence of Machine Learning and Communications ITU Journal: ICT Discoveries - Special Issue 1 - The Impact of Artificial Intelligence (AI) on Communication Networks and Services, 1(1):49-58, 2018 [bibtex] [pdf] [url]

19.	Wojciech Samek, Shinichi Nakajima, Motoaki Kawanabe, and Klaus-Robert Müller: On Robust Parameter Estimation in Brain-Computer Interfacing Journal of Neural Engineering, 14(6):061001, 2017 [bibtex] [pdf] [url]

18.	Leila Arras, Franziska Horn, Grégoire Montavon, Klaus-Robert Müller, and Wojciech Samek: "What is Relevant in a Text Document?": An Interpretable Machine Learning Approach PLOS ONE, 12(8):e0181142, 2017 [bibtex] [pdf] [url]

17.	Forooz Shahbazi Avarvand, Sebastian Bosse, Klaus-Robert Müller, Guido Nolte, Thomas Wiegand, Ralf Schäfer, Gabriel Curio, and Wojciech Samek: Objective Quality Assessment of Stereoscopic Images with Vertical Disparity using EEG Journal of Neural Engineering, 14(4):046009, 2017 [bibtex] [pdf] [url]

16.	Grégoire Montavon, Sebastian Lapuschkin, Alexander Binder, Wojciech Samek, and Klaus-Robert Müller: Explaining Nonlinear Classification Decisions with Deep Taylor Decomposition Pattern Recognition, 65:211-222, 2017 [bibtex] [pdf] [url] [supplement]

15.	Wojciech Samek, Alexander Binder, Grégoire Montavon, Sebastian Lapuschkin, and Klaus-Robert Müller: Evaluating the Visualization of What a Deep Neural Network has Learned IEEE Transactions on Neural Networks and Learning Systems, 28(11):2660-2673, 2017 [bibtex] [pdf] [url]

14.	Irene Sturm, Sebastian Lapuschkin, Wojciech Samek, and Klaus-Robert Müller: Interpretable Deep Neural Networks for Single-Trial EEG Classification Journal of Neuroscience Methods, 274:141145, 2016 [bibtex] [pdf] [url]

13.	Stephanie Brandl, Laura Frølich, Johannes Höhne, Klaus-Robert Müller, and Wojciech Samek: Brain-Computer Interfacing under Distraction: An Evaluation Study Journal of Neural Engineering, 13(5):056012, 2016 [bibtex] [pdf] [url]

12.	Wojciech Samek, Duncan Blythe, Gabriel Curio, Klaus-Robert Müller, Benjamin Blankertz, and Vadim V Nikulin: Multiscale Temporal Neural Dynamics Predict Performance in a Complex Sensorimotor Task NeuroImage, 141:291303, 2016 [bibtex] [pdf] [url]

11.	Wojciech Samek: On Robust Spatial Filtering of EEG in Nonstationary Environments it-Information Technology, Distinguished Dissertations, 58(3):150-154, 2016 [bibtex] [pdf] [url]

10.	Sebastian Lapuschkin, Alexander Binder, Grégoire Montavon, Klaus-Robert Müller, and Wojciech Samek: The Layer-wise Relevance Propagation Toolbox for Artificial Neural Networks Journal of Machine Learning Research, 17(114):1-5, 2016 [bibtex] [pdf] [url]

9.	Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus-Robert Müller, and Wojciech Samek: On Pixel-wise Explanations for Non-Linear Classifier Decisions by Layer-wise Relevance Propagation PLOS ONE, 10(7):e0130140, 2015 [bibtex] [pdf] [url]

Sven Dähne, Felix Bießmann, Wojciech Samek, Stefan Haufe, Dominique Goltz, Christopher Gundlach, Arno Villringer, Siamac Fazli, and Klaus-Robert Müller:
Multivariate Machine Learning Methods for Fusing Multimodal Functional Neuroimaging Data
Proceedings of the IEEE, 103(9):1507-30, 2015
[bibtex] [pdf] [url]

7.	Siamac Fazli, Sven Dähne, Wojciech Samek, Felix Bießmann, and Klaus-Robert Müller: Learning from more than one Data Source: Data Fusion Techniques for Sensorimotor Rhythm-based Brain-Computer Interfaces Proceedings of the IEEE, 103(6):891-906, 2015 [bibtex] [pdf] [url]

6.	Wojciech Samek, Motoaki Kawanabe, and Klaus-Robert Müller: Divergence-based Framework for Common Spatial Patterns Algorithms IEEE Reviews in Biomedical Engineering, 7:50-72, 2014 [bibtex] [pdf] [url]

5.	Motoaki Kawanabe, Wojciech Samek, Klaus-Robert Müller, and Carmen Vidaurre: Robust Common Spatial Filters with a Maxmin Approach Neural Computation, 26(2):349-376, 2014 [bibtex] [pdf] [url]

4.	Wojciech Samek, Frank C. Meinecke, and Klaus-Robert Müller: Transferring Subspaces Between Subjects in Brain-Computer Interfacing IEEE Transactions on Biomedical Engineering, 60(8):2289-98, 2013 [bibtex] [pdf] [url]

3.	Alexander Binder, Wojciech Samek, Klaus-Robert Müller, and Motoaki Kawanabe: Enhanced Representation and Multi-Task Learning for Image Annotation Computer Vision and Image Understanding, 117(5):466-78, 2013 [bibtex] [pdf] [url]

2.	Wojciech Samek, Carmen Vidaurre, Klaus-Robert Müller, and Motoaki Kawanabe: Stationary Common Spatial Patterns for Brain-Computer Interfacing Journal of Neural Engineering, 9(2):026013, 2012 [bibtex] [pdf] [url]

1.	Alexander Binder, Shinichi Nakajima, Marius Kloft, Christina Müller, Wojciech Samek, Ulf Brefeld, Klaus-Robert Müller, and Motoaki Kawanabe: Insights from Classifying Visual Concepts with Multiple Kernel Learning PLOS ONE, 7(8):e38897, 2012 [bibtex] [pdf] [url]

Conference and Workshop Papers

119.	Maximilian Andreas Höfler, Tatsiana Mazouka, Karsten Mueller, and Wojciech Samek: Boosting Federated Learning with Diffusion Models for Non-IID and Imbalanced Data IEEE International Conference on Big Data, 2024 [bibtex] [pdf] [url]

118.

Dilyara Bareeva, Galip Ümit Yolcu, Anna Hedström, Niklas Schmolenski, Thomas Wiegand, Wojciech Samek, Sebastian Lapuschkin:
Quanda: An Interpretability Toolkit for Training Data Attribution Evaluation and Beyond
NeurIPS'24 Workshop on Attributing Model Behavior at Scale (ATTRIB), 2024
[bibtex] [pdf] [url]

117.	Jonas R. Naujoks, Aleksander Krasowski, Moritz Weckbecker, Thomas Wiegand, Sebastian Lapuschkin, Wojciech Samek, René P. Klausen: PINNfluence: Influence Functions for Physics-Informed Neural Networks NeurIPS'24 Workshop on Machine Learning and the Physical Sciences, 2024 [bibtex] [pdf] [url]

116.

Rohan Reddy Mekala, Frederik Pahde, Simon Baur, Sneha Chandrashekar, Madeline Diep, Markus Wenzel, Eric L. Wisotzky, Galip Ümit Yolcu, Sebastian Lapuschkin, Jackie Ma, Peter Eisert, Mikael Lindvall, Adam Porter, Wojciech Samek:
Synthetic Generation of Dermatoscopic Images with GAN and Closed-Form Factorization
ECCV'24 Workshop on Synthetic Data for Computer Vision (SyntheticData4CV), 2024
[bibtex] [pdf] [url]

115.	Michael Detzel, Gabriel Nobis, Jackie Ma, Wojciech Samek: Spatial Shortcuts in Graph Neural Controlled Differential Equations NeurIPS'24 Workshop on Data-driven and Differentiable Simulations, Surrogates, and Solvers (D3S3), 2024 [bibtex] [pdf] [url]

114.

Gabriel Nobis, Maximilian Springenberg, Marco Aversa, Michael Detzel, Rembert Daems, Roderick Murray-Smith, Shinichi Nakajima, Sebastian Lapuschkin, Stefano Ermon, Tolga Birdal, Manfred Opper, Christoph Knochenhauer, Luis Oala, Wojciech Samek:
Generative Fractional Diffusion Models
Advances in Neural Information Processing Systems 37 (NeurIPS), 2024
[bibtex] [pdf] [url]

113.	Sayed M. V. Hatefi, Maximilian Dreyer, Reduan Achtibat, Thomas Wiegand, Wojciech Samek, Sebastian Lapuschkin: Pruning By Explaining Revisited: Optimizing Attribution Methods to Prune CNNs and Transformers ECCV'24 Workshop on Explainable Computer Vision (eXCV), 2024 [bibtex] [pdf] [url]

112.	Maximilian A. Höfler, Karsten Müller, Wojciech Samek: XAI-guided Insulator Anomaly Detection for Imbalanced Datasets ECCV'24 Workshop on Vision-Based Industrial Inspection (VISION), 2024 [bibtex] [pdf] [url]

111.

Gabriel Nobis, Maximilian Springenberg, Marco Aversa, Michael Detzel, Rembert Daems, Roderick Murray-Smith, Shinichi Nakajima, Sebastian Lapuschkin, Stefano Ermon, Tolga Birdal, Manfred Opper, Christoph Knochenhauer, Luis Oala, Wojciech Samek:
Generative Fractional Diffusion Models
ICML'24 Workshop on Structured Probabilistic Inference & Generative Modeling, 2024
[bibtex] [pdf] [url]

110.	Leon Witt, Armando Teles Fortes, Kentaroh Toyoda, Wojciech Samek and Dan Li: Blockchain and Artificial Intelligence: Synergies and Conflicts Proceedings of the Workshop on Blockchain and Artificial Intelligence (BCAI), 2024 * Best paper award * [bibtex] [pdf] [url]

109.	Leon Witt, Kentaroh Toyoda, Wojciech Samek and Dan Li: Democratizing Federated Learning with Blockchain and Multi-Task Peer Prediction Proceedings of the Workshop on Blockchain and Artificial Intelligence (BCAI), 2024 [bibtex] [pdf] [url]

108.

Reduan Achtibat, Sayed M. V. Hatefi, Maximilian Dreyer, Aakriti Jain, Thomas Wiegand, Sebastian Lapuschkin, Wojciech Samek:
AttnLRP: Attention-Aware Layer-wise Relevance Propagation for Transformers
Proceedings of the 41st International Conference on Machine Learning (ICML), PMLR, 235:135-168, 2024
[bibtex] [pdf] [url]

107.	Przemyslaw Biecek and Wojciech Samek: Position: Explain to Question not to Justify Proceedings of the 41st International Conference on Machine Learning (ICML), PMLR, 235:3996-4006, 2024 [bibtex] [pdf] [url]

106.	Xiaoyan Yu, Jannik Franzen, Wojciech Samek, Marina Höhne, Dagmar Kainmüller: Model guidance via explanations turns image classifiers into segmentation models Explainable Artificial Intelligence, Second World Conference, xAI 2024, 2154:113-129, Springer, Cham, 2024 [bibtex] [pdf] [url]

105.

Christian Tinauer, Anna Damulina, Maximilian Sackl, Martin Soellradl, Reduan Achtibat, Maximilian Dreyer, Frederik Pahde, Sebastian Lapuschkin, Reinhold Schmidt, Stefan Ropele, Wojciech Samek and Christian Langkammer:
Explainable Concept Mappings of MRI: Revealing the Mechanisms Underlying Deep Learning-Based Brain Disease Classification
Explainable Artificial Intelligence, Second World Conference, xAI 2024, 2154:202-216, Springer, Cham, 2024
[bibtex] [pdf] [url]

104.

Dilyara Bareeva, Maximilian Dreyer, Frederik Pahde, Wojciech Samek, Sebastian Lapuschkin:
Reactive Model Correction: Mitigating Harm to Task-Relevant Features via Conditional Bias Suppression
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 3532-3541, 2024
[bibtex] [pdf] [url]

103.

Maximilian Dreyer, Reduan Achtibat, Wojciech Samek, Sebastian Lapuschkin:
Understanding the (Extra-)Ordinary: Validating Deep Model Decisions with Prototypical Concept-based Explanations
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 3491-3501, 2024
[bibtex] [pdf] [url]

102.

Maximilian Dreyer, Erblina Purelku, Johanna Vielhaben, Wojciech Samek, Sebastian Lapuschkin:
PURE: Turning Polysemantic Neurons Into Pure Features by Identifying Relevant Circuits
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2024
[bibtex] [pdf] [url]

101.	Bernhard Föllmer, Kenrick Schulze, Christian Wald, Sebastian Stober, Wojciech Samek, Marc Dewey: Active Learning with the nnUNet and Sample Selection with Uncertainty-Aware Submodular Mutual Information Measure Proceedings of the Medical Imaging with Deep Learning (MIDL), 2024 [bibtex] [pdf] [url]

100.

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
Proceedings of the Thirty-Eight AAAI Conference on Artificial Intelligence, 38(19):21046-21054, 2024
[bibtex] [pdf] [url]

99.	Johanna Vielhaben, Sebastian Lapuschkin, Grégoire Montavon, Wojciech Samek: Explainable AI for Audio via Virtual Inspection Layers NeurIPS'23 Workshop on Machine Learning for Audio, 2023 [bibtex] [pdf] [url]

98.	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 NeurIPS'23 Workshop on Diffusion, 2023 [bibtex] [pdf] [url]

97.	Karm Dawoud, Wojciech Samek, Sebastian Lapuschkin, Sebastian Bosse: Human-Centered Evaluation of XAI Methods Proceedings of the ICDM'23 Workshop on Causal and Explainable Artificial Intelligence (CXAI), 912-921, 2023 [bibtex] [pdf] [url]

96.

Marco Aversa, Gabriel Nobis, Miriam Hägele, Kai Standvoss, Mihaela Chirica, Roderick Murray-Smith, Ahmed Alaa, Lukas Ruff, Daniela Ivanova, Wojciech Samek, Frederick Klauschen, Bruno Sanguinetti, Luis Oala:
DiffInfinite: Large Mask-Image Synthesis via Parallel Random Patch Diffusion in Histopathology
Advances in Neural Information Processing Systems 36 (NeurIPS), 78126-78141, 2023
[bibtex] [pdf] [url]

95.

Luis Oala, Marco Aversa, Gabriel Nobis, Kurt Willis, Yoan Neuenschwander, Michèle Buck, Christian Matek, Jerome Extermann, Enrico Pomarico, Wojciech Samek, Roderick Murray-Smith, Christoph Clausen, Bruno Sanguinetti:
Data Models for Dataset Drift Controls in Machine Learning With Optical Images
Proceedings of the ICML'23 Workshop on Spurious correlations, Invariance, and Stability (SCIS), 2023
[bibtex] [pdf] [url]

94.	Daniel Becking, Paul Haase, Heiner Kirchhoffer, Karsten Müller, Wojciech Samek, Detlev Marpe: NNCodec: An Open Source Software Implementation of the Neural Network Coding ISO/IEC Standard Proceedings of the ICML'23 Workshop on Neural Compression, 2023 [bibtex] [pdf] [url]

93.	Annika Frommholz, Fabian Seipel, Sebastian Lapuschkin, Wojciech Samek, Johanna Vielhaben: XAI-based Comparison of Input Representations for Audio Event Classification Proceedings of the 20th International Conference on Content-based Multimedia Indexing, 126-132, 2023 [bibtex] [pdf] [url]

92.

Frederik Pahde, Maximilian Dreyer, Wojciech Samek, Sebastian Lapuschkin:
Reveal to Revise: An Explainable AI Life Cycle for Iterative Bias Correction of Deep Models
Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. LNCS, 14221:596-606, Springer, Cham, 2023
[bibtex] [pdf] [url]

91.

Frederik Pahde, Galip Ümit Yolcu, Alexander Binder, Wojciech Samek, Sebastian Lapuschkin:
Optimizing Explanations by Network Canonization and Hyperparameter Search
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 3818-3827, 2023
[bibtex] [pdf] [url]

90.

Maximilian Dreyer, Reduan Achtibat, Thomas Wiegand, Wojciech Samek, Sebastian Lapuschkin:
Revealing Hidden Context Bias in Segmentation and Object Detection through Concept-specific Explanations
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 3828-3838, 2023
[bibtex] [pdf] [url]

89.

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
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 16143-16152, 2023
[bibtex] [pdf] [url]

88.

Gerhard Tech, Paul Haase, Daniel Becking, Heiner Kirchhoffer, Karsten Müller, Jonathan Pfaff, Heiko Schwarz, Wojciech Samek, Detlev Marpe, Thomas Wiegand:
History Dependent Significance Coding for Incremental Neural Network Compression
Proceedings of the IEEE International Conference on Image Processing (ICIP), 3541-3545, 2022
[bibtex] [pdf] [url]

87.	Haley Hoech, Roman Rischke, Karsten Müller, Wojciech Samek: FedAUXfdp: Differentially Private One-Shot Federated Distillation Trustworthy Federated Learning, Lecture Notes in Computer Science, 13448:100-114, 2023 [bibtex] [pdf] [url]

86.

Sami Ede, Serop Baghdadlian, Leander Weber, An Nguyen, Dario Zanca, Wojciech Samek, Sebastian Lapuschkin:
Explain to Not Forget: Defending Against Catastrophic Forgetting with XAI
Machine Learning and Knowledge Extraction. CD-MAKE 2022, Lecture Notes in Computer Science, Springer, 13480:1-18, 2022
[bibtex] [pdf] [url]

85.

Daniel Becking, Heiner Kirchhoffer, Gerhard Tech, Paul Haase, Karsten Müller, Heiko Schwarz, and Wojciech Samek:
Adaptive Differential Filters for Fast and Communication-Efficient Federated Learning
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 3366-3375, 2022
[bibtex] [pdf] [url]

85.

Paul Haase, Daniel Becking, Heiner Kirchhoffer, Karsten Müller, Heiko Schwarz, Wojciech Samek, Detlev Marpe, and Thomas Wiegand:
Encoder Optimizations for the NNR Standard on Neural Network Compression
Proceedings of the IEEE International Conference on Image Processing (ICIP), 3522-3526, 2021
[bibtex] [pdf] [url]

84.	Jan Macdonald, Maximilian März, Luis Oala, and Wojciech Samek: Interval Neural Networks as Instability Detectors for Image Reconstructions Bildverarbeitung für die Medizin, 324-329, 2021 [bibtex] [pdf] [url]

83.	Jiamei Sun, Sebastian Lapuschkin, Wojciech Samek, Yunqing Zhao, Ngai-Man Cheung, and Alexander Binder: Explanation-Guided Training for Cross-Domain Few-Shot Classification Proceedings of the 25th International Conference on Pattern Recognition (ICPR), 7609-7616, 2021 [bibtex] [pdf] [url]

82.	Christopher J. Anders, David Neumann, Talmaj Marin, Wojciech Samek, Klaus-Robert Müller, and Sebastian Lapuschkin: XAI for Analyzing and Unlearning Spurious Correlations in ImageNet ICML'20 Workshop on Extending Explainable AI Beyond Deep Models and Classifiers (XXAI), 2020 [bibtex] [pdf] [url]

81.	Jiamei Sun, Sebastian Lapuschkin, Wojciech Samek, Yunqing Zhao, Ngai-Man Cheung, and Alexander Binder: Explain and Improve: Cross-Domain-Few-Shot-Learning Using Explanations ICML'20 Workshop on Extending Explainable AI Beyond Deep Models and Classifiers (XXAI), 2020 [bibtex] [pdf] [url]

80.	Jiamei Sun, Sebastian Lapuschkin, Wojciech Samek, and Alexander Binder: Understanding Image Captioning Models beyond Visualizing Attention ICML'20 Workshop on Extending Explainable AI Beyond Deep Models and Classifiers (XXAI), 2020 [bibtex] [pdf] [url]

79.	Luis Oala, Cosmas Heiß, Jan Macdonald, Maximilian März, Wojciech Samek, and Gitta Kutyniok: Detecting Failure Modes in Image Reconstructions with Interval Neural Network Uncertainty Proceedings of the ICML'20 Workshop on Uncertainty & Robustness in Deep Learning, 2020 [bibtex] [pdf] [url]

78.	Gary S. W. Goh, Sebastian Lapuschkin, Leander Weber, Wojciech Samek, and Alexander Binder: Understanding Integrated Gradients with SmoothTaylor for Deep Neural Network Attribution Proceedings of the 25th International Conference on Pattern Recognition (ICPR), 4949-4956, 2021 [bibtex] [pdf] [url]

77.

David Neumann, Felix Sattler, Heiner Kirchhoffer, Simon Wiedemann, Karsten Müller, Heiko Schwarz, Thomas Wiegand, Detlev Marpe, and Wojciech Samek:
DeepCABAC: Plug&Play Compression of Neural Network Weights and Weight Updates
Proceedings of the IEEE International Conference on Image Processing (ICIP), 21-25, 2020
[bibtex] [pdf] [url]

76.

Paul Haase, Heiko Schwarz, Heiner Kirchhoffer, Simon Wiedemann, Talmaj Marinc, Arturo Marban, Karsten Müller, Wojciech Samek, Detlev Marpe, and Thomas Wiegand:
Dependent Scalar Quantization for Neural Network Compression
Proceedings of the IEEE International Conference on Image Processing (ICIP), 36-40, 2020
[bibtex] [pdf] [url]

75.

Simon Wiedemann, Temesgen Mehari, Kevin Kepp, and Wojciech Samek:
Dithered backprop: A sparse and quantized backpropagation algorithm for more efficient deep neural network training
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 3096-3104, 2020
[bibtex] [pdf] [url]

74.

Arturo Marban, Daniel Becking, Simon Wiedemann, and Wojciech Samek:
Learning Sparse & Ternary Neural Networks with Entropy-Constrained Trained Ternarization (EC2T)
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 3105-3113, 2020
[bibtex] [pdf] [url]

73.

Maximilian Kohlbrenner, Alexander Bauer, Shinichi Nakajima, Alexander Binder, Wojciech Samek, and Sebastian Lapuschkin:
Towards best practice in explaining neural network decisions with LRP
Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN), 1-7, 2020
[bibtex] [pdf] [url]

72.	Felix Sattler, Klaus-Robert Müller, Thomas Wiegand, and Wojciech Samek: On the Byzantine Robustness of Clustered Federated Learning Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 8861-8865, 2020 [bibtex] [pdf] [url]

71.	Vignesh Srinivasan, Klaus-Robert Müller, Wojciech Samek, and Shinichi Nakajima: Benign Examples: Imperceptible Changes Can Enhance Image Translation Performance Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 5842-5850, 2020 [bibtex] [pdf] [url]

70.	Felix Sattler, Klaus-Robert Müller, and Wojciech Samek: Clustered Federated Learning Proceedings of the NeurIPS'19 Workshop on Federated Learning for Data Privacy and Confidentiality, 1-5, 2019 [bibtex] [pdf] [url]

69.

Johanna Vielhaben, Hüseyin Camalan, Wojciech Samek, and Markus Wenzel:
Field of View Forecasting in Virtual Reality with Machine Learning
Proceedings of the IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR), 74-81, 2019
*** Honorable mention ***
[bibtex] [pdf] [url]

68.	Armin W. Thomas, Klaus-Robert Müller, and Wojciech Samek: Deep Transfer Learning For Whole-Brain fMRI Analyses OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging, Lecture Notes in Computer Science, Springer, 11796:59-67, 2019 [bibtex] [pdf] [url]

67.

Jan Laermann, Wojciech Samek, and Nils Strodthoff:
Achieving Generalizable Robustness of Deep Neural Networks by Stability Training
Pattern Recognition - 41st DAGM German Conference, DAGM GCPR 2019, Lecture Notes in Computer Science, Springer International Publishing, 360-373, 2019
[bibtex] [pdf] [url]

66.	Vignesh Srinivasan, Ercan E. Kuruoglu, Klaus-Robert Müller, Wojciech Samek, and Shinichi Nakajima: Black-Box Decision based Adversarial Attack with Symmetric alpha-stable Distribution Proceedings of the European Signal Processing Conference (EUSIPCO), 1-5, 2019 [bibtex] [pdf] [url]

65.	Leila Arras, Ahmed Osman, Klaus-Robert Müller and Wojciech Samek: Evaluating Recurrent Neural Network Explanations Proceedings of the ACL'19 Workshop on BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, 113-126, 2019 [bibtex] [pdf] [url]

64.	Vignesh Srinivasan, Arturo Marban, Klaus-Robert Müller, Wojciech Samek, and Shinichi Nakajima: Robustifying Models Against Adversarial Attacks by Langevin Dynamics ICML'19 Workshop on Uncertainty & Robustness in Deep Learning, 1-6, 2019 [bibtex] [pdf] [url]

63.	Talmaj Marinc, Vignesh Srinivasan, Serhan Gül, Cornelius Hellge, and Wojciech Samek: Multi-Kernel Prediction Networks for Denoising of Burst Images Proceedings of the IEEE International Conference on Image Processing (ICIP), 2404-2408, 2019 [bibtex] [pdf] [url]

62.

Simon Wiedemann, Heiner Kirchhoffer, Stefan Matlage, Paul Haase, Arturo Marban, Talmaj Marinc, David Neumann, Ahmed Osman, Detlev Marpe, Heiko Schwarz, Thomas Wiegand, and Wojciech Samek:
DeepCABAC: Context-adaptive binary arithmetic coding for deep neural network compression
Joint ICML'19 Workshop on On-Device Machine Learning & Compact Deep Neural Network Representations (ODML-CDNNR), 1-4, 2019
*** Best paper award ***
[bibtex] [pdf] [url] [code]

61.	Patrick Wagner, Jakob P. Morath, Arturo Zychlinsky, and Wojciech Samek: Rotation Invariant Clustering of 3D Cell Nuclei Shapes Proceedings of 41th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 6022-6027, 2019 [bibtex] [pdf] [url]

60.	Simon Wiedemann, Arturo Marban, Klaus-Robert Müller, and Wojciech Samek: Entropy-Constrained Training of Deep Neural Networks Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN), 1-8, 2019 [bibtex] [pdf] [url]

59.	Felix Sattler, Simon Wiedemann, Klaus-Robert Müller, and Wojciech Samek: Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communication Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN), 1-8, 2019 [bibtex] [pdf] [url]

58.	Simon Wiedemann, Klaus-Robert Müller, and Wojciech Samek: Compact and Computationally Efficient Representation of Deep Neural Networks NIPS Workshop on Compact Deep Neural Network Representation with Industrial Applications (CDNNRIA), 1-8, 2018 [bibtex] [pdf] [url]

57.

Maximilian Alber, Sebastian Lapuschkin, Philipp Seegerer, Miriam Hägele, Kristof T. Schütt, Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller, Sven Dähne, and Pieter-Jan Kindermans:
How to iNNvestigate neural networks predictions!
NIPS Workshop on Machine Learning Open Source Software (MLOSS), 1-6, 2018
[bibtex] [pdf] [url]

56.	Nils Strodthoff, Baris Göktepe, Thomas Schierl, Wojciech Samek and Cornelius Hellge: Machine Learning for early HARQ Feedback Prediction in 5G Proceedings of the IEEE Globecom Workshops (GC Wkshps), 1-6, 2018 [bibtex] [pdf] [url]

55.

Jonathan Pfaff, Phillip Helle, Dominique Maniry, Stephan Kaltenstadler, Wojciech Samek, Heiko Schwarz, Detlev Marpe, and Thomas Wiegand:
Neural Network based Intra Prediction for Video Coding
Proceedings of the SPIE 10752, Applications of Digital Image Processing XLI, 1075213, 2018
[bibtex] [pdf] [url]

54.

Arturo Marban, Vignesh Srinivasan, Wojciech Samek, Josep Fernández, and Alicia Casals:
Estimation of Interaction Forces in Robotic Surgery using a Semi-Supervised Deep Neural Network Model
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots (IROS), 761-768, 2018
[bibtex] [pdf] [url]

53.	Sebastian Bosse, Sören Becker, Zacharias V. Fisches, Wojciech Samek, and Thomas Wiegand: Neural Network-based Estimation of Distortion Sensitivity for Image Quality Prediction Proceedings of the IEEE International Conference on Image Processing (ICIP), 629-633, 2018 [bibtex] [pdf] [url]

52.	Sebastian Bosse, Milena Bagdasarian, Wojciech Samek, Gabriel Curio, and Thomas Wiegand: On the Stimulation Frequency in SSVEP-based Image Quality Assessment Proceedings of the 10th International Conference on Quality of Multimedia Experience (QoMEX), 1-6, 2018 [bibtex] [pdf] [url]

51.	Christopher Ehmann and Wojciech Samek: Transferring Information between Neural Networks Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2361-2365, 2018 [bibtex] [pdf] [url]

50.	Sebastian Lapuschkin, Alexander Binder, Klaus-Robert Müller, and Wojciech Samek: Understanding and Comparing Deep Neural Networks for Age and Gender Classification Proceedings of the IEEE International Conference on Computer Vision Workshops (ICCVW), 1629-38, 2017 [bibtex] [pdf] [url] [code]

49.

Arturo Marban, Vignesh Srinivasan, Wojciech Samek, Josep Fernández, and Alicia Casals:
Estimating Position & Velocity in 3D Space from Monocular Video Sequences using a Deep Neural Network
Proceedings of the IEEE International Conference on Computer Vision Workshops (ICCVW), 1460-69, 2017
[bibtex] [pdf] [url]

48.

Leila Arras, Grégoire Montavon, Klaus-Robert Müller, and Wojciech Samek:
Explaining Recurrent Neural Network Predictions in Sentiment Analysis
Proceedings of the EMNLP'17 Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis (WASSA), 159-168, 2017
[bibtex] [pdf] [url] [code]

47.

Jing Yu Koh, Wojciech Samek, Klaus-Robert Müller and Alexander Binder:
Object Boundary Detection and Classification with Image-level Labels
Pattern Recognition - 39th German Conference, GCPR 2017, Lecture Notes in Computer Science, 10496:153-164, Springer International Publishing, 2017
[bibtex] [pdf] [url]

46.

Clemens Seibold, Wojciech Samek, Anna Hilsmann, and Peter Eisert:
Detection of Face Morphing Attacks by Deep Learning
Digital Forensics and Watermarking: 16th International Workshop, IWDW 2017, Lecture Notes in Computer Science, 10431:107-120, Springer International Publishing, 2017
[bibtex] [pdf] [url]

45.	Sebastian Bosse, Mischa Siekmann, Wojciech Samek, and Thomas Wiegand: A Perceptually Relevant Shearlet-Based Adaptation of the PSNR Proceedings of the IEEE International Conference on Image Processing (ICIP), 315-19, 2017 [bibtex] [pdf] [url]

44.

Forooz Shahbazi Avarvand, Sebastian Bosse, Guido Nolte, Thomas Wiegand, and Wojciech Samek:
Quality Assessment of 3D Visualizations with Vertical Disparity: An ERP Approach
Proceedings of 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 4391-94, 2017
[bibtex] [pdf] [url]

43.	Forooz Shahbazi Avarvand, Sebastian Bosse, Guido Nolte, Thomas Wiegand, and Wojciech Samek: Measuring the Quality of 3D Visualizations using EEG: A Time-Frequency Approach Proceedings of 7th Graz Brain-Computer Interface Conference (GBCI), Verlag der TU Graz, 441-46, 2017 [bibtex] [pdf] [url]

42.	Vignesh Srinivasan, Sebastian Lapuschkin, Cornelius Hellge, Klaus-Robert Müller, and Wojciech Samek: Interpretable Human Action Recognition in Compressed Domain Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1692-96, 2017 [bibtex] [pdf] [url]

41.	Wojciech Samek, Grégoire Montavon, Alexander Binder, Sebastian Lapuschkin, and Klaus-Robert Müller: Interpreting the Predictions of Complex ML Models by Layer-wise Relevance Propagation Proceedings of the Interpretable ML for Complex Systems NIPS'16 Workshop, 1-5, 2016 [bibtex] [pdf] [url]

40.	Sebastian Bosse, Dominique Maniry, Klaus-Robert Müller, Thomas Wiegand, and Wojciech Samek: Neural Network-Based Full-Reference Image Quality Assessment Proceedings of the Picture Coding Symposium (PCS), 1-5, 2016 [bibtex] [pdf] [url]

39.

Vignesh Srinivasan, Serhan Gül, Sebastian Bosse, Jan T. Meyer, Thomas Schierl, Cornelius Hellge, and Wojciech Samek:
On the Robustness of Action Recognition Methods in Compressed and Pixel Domain
Proceedings of the European Workshop on Visual Information Processing (EUVIP), 1-6, 2016
[bibtex] [pdf] [url]

38.	Serhan Gül, Jan T. Meyer, Cornelius Hellge, Thomas Schierl, and Wojciech Samek: Hybrid Video Object Tracking in H.265/HEVC Video Streams Proceedings of the International Workshop on Multimedia Signal Processing (MMSP), 1-5, 2016 [bibtex] [pdf] [url]

37.	Grégoire Montavon, Sebastian Bach, Alexander Binder, Wojciech Samek, and Klaus-Robert Müller: Deep Taylor Decomposition of Neural Networks Proceedings of the ICML'16 Workshop on Visualization for Deep Learning, 1-3, 2016 [bibtex] [pdf] [url]

36.	Alexander Binder, Wojciech Samek, Grégoire Montavon, Sebastian Bach, and Klaus-Robert Müller: Analyzing and Validating Neural Networks Predictions Proceedings of the ICML'16 Workshop on Visualization for Deep Learning, 1-4, 2016 * Best paper prize * [bibtex] [pdf][url]

35.	Sebastian Bosse, Dominique Maniry, Klaus-Robert Müller, Thomas Wiegand, and Wojciech Samek: Full-Reference Image Quality Assessment Using Neural Networks Proceedings of the International Conference on Quality of Multimedia Experience (QoMEX), short paper, 2016 [bibtex] [pdf]

34.

Alexander Binder, Grégoire Montavon, Sebastian Lapuschkin, Klaus-Robert Müller, and Wojciech Samek:
Layer-wise Relevance Propagation for Neural Networks with Local Renormalization Layers
Artificial Neural Networks and Machine Learning ICANN 2016, Part II, Lecture Notes in Computer Science, Springer-Verlag, 9887:63-71, 2016
[bibtex] [pdf] [url]

33.	Leila Arras, Franziska Horn, Grégoire Montavon, Klaus-Robert Müller, and Wojciech Samek: Explaining Predictions of Non-Linear Classifiers in NLP Proceedings of the ACL'16 Workshop on Representation Learning for NLP, 1-7, 2016 [bibtex] [pdf] [url]

32.	Stephanie Brandl, Klaus-Robert Müller, and Wojciech Samek: Alternative CSP Approaches for Multimodal Distributed BCI Data Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC), 003742-003747, 2016 [bibtex] [pdf] [url]

31.	Sebastian Bosse, Klaus-Robert Müller, Thomas Wiegand, and Wojciech Samek: Brain-Computer Interfacing for Multimedia Quality Assessment Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC), 002834-002839, 2016 [bibtex] [pdf] [url]

30.

Farhad Arbabzadah, Grégoire Montavon, Klaus-Robert Müller, and Wojciech Samek:
Identifying Individual Facial Expressions by Deconstructing a Neural Network
Pattern Recognition - 38th German Conference, GCPR 2016, Lecture Notes in Computer Science, 9796:344-54, Springer International Publishing, 2016
[bibtex] [pdf] [url]

29.	Sebastian Bach, Alexander Binder, Klaus-Robert Müller, and Wojciech Samek: Controlling Explanatory Heatmap Resolution and Semantics via Decomposition Depth Proceedings of the IEEE International Conference on Image Processing (ICIP), 2271-75, 2016 [bibtex] [pdf] [url]

28.	Sebastian Bosse, Dominique Maniry, Thomas Wiegand, and Wojciech Samek: A Deep Neural Network for Image Quality Assessment Proceedings of the IEEE International Conference on Image Processing (ICIP), 3773-77, 2016 [bibtex] [pdf] [url]

27.	Sebastian Bosse, Qiaobo Chen, Mischa Siekmann, Wojciech Samek, and Thomas Wiegand: Shearlet-based Reduced Reference Image Quality Assessment Proceedings of the IEEE International Conference on Image Processing (ICIP), 2052-56, 2016 [bibtex] [pdf] [url]

26.	Sebastian Bosse, Mischa Siekmann, Jennifer Rasch, Thomas Wiegand, and Wojciech Samek: Quality Assessment of Image Patches Distorted by Image Compression Using Crowdsourcing Proceedings of the IEEE International Conference on Multimedia and Expo (ICME), 1-6, 2016 [bibtex] [pdf] [url]

25.

Alexander Binder, Sebastian Bach, Grégoire Montavon, Klaus-Robert Müller, and Wojciech Samek:
Layer-wise Relevance Propagation for Deep Neural Network Architectures
Information Science and Applications (ICISA) 2016, Lecture Notes in Electrical Engineering, 376:913-22, Springer Singapore, 2016
[bibtex] [pdf] [url]

24.

Sebastian Lapuschkin, Alexander Binder, Grégoire Montavon, Klaus-Robert Müller, and Wojciech Samek:
Analyzing Classifiers: Fisher Vectors and Deep Neural Networks
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2912-20, 2016
[bibtex] [pdf] [url] [supplement] [code]

23.	Laura Frølich, Irene Winkler, Klaus-Robert Müller, and Wojciech Samek: Investigating Effects of Different Artefact Types on Motor Imagery BCI Proceedings of 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 1942-45, 2015 [bibtex] [pdf] [url]

22.	Wojciech Samek and Klaus-Robert Müller: Tackling Noise, Artifacts and Nonstationarity in BCI with Robust Divergences Proceedings of the European Signal Processing Conference (EUSIPCO), 2791-95, 2015 [bibtex] [pdf] [url]

21.	Carmen Vidaurre, Claudia Sannelli, Wojciech Samek, Sven Dähne, and Klaus-Robert Müller: Machine Learning Methods of the Berlin Brain-Computer Interface Proceedings of 9th IFAC Symposium on Biological and Medical Systems (BMS), IFAC-PapersOnLine, 48(20):447-52, 2015 [bibtex] [pdf] [url]

20.	Stephanie Brandl, Johannes Höhne, Klaus-Robert Müller, and Wojciech Samek: Bringing BCI into Everyday Life: Motor imagery in a Pseudo Realistic Environment Proceedings of the 7th International IEEE/EMBS Neural Engineering Conference (NER), 224-27, 2015 [bibtex] [pdf] [url]

19.	Stephanie Brandl, Klaus-Robert Müller, and Wojciech Samek: Robust Common Spatial Patterns based on Bhattacharyya Distance and Gamma Divergence Proceedings of the 3rd IEEE International Winter Workshop on Brain-Computer Interface (BCI), 1-4, 2015 [bibtex] [pdf] [url]

18.	Wojciech Samek and Motoaki Kawanabe: Robust Common Spatial Patterns by Minimum Divergence Covariance Estimator Proceedings of 39th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2059-62, 2014 [bibtex] [pdf] [url]

17.	Wojciech Samek and Klaus-Robert Müller: Information Geometry meets BCI - Spatial Filtering using Divergences Proceedings of the 2nd IEEE International Winter Workshop on Brain-Computer Interface (BCI), 1-4, 2014 [bibtex] [pdf] [url]

16.	Wojciech Samek, Duncan Blythe, Klaus-Robert Müller, and Motoaki Kawanabe: Robust Spatial Filtering with Beta Divergence Advances in Neural Information Processing Systems 26 (NIPS), 1007-15, 2013 * Spotlight paper * [bibtex] [pdf] [url] [supplement] [video]

15.	Wojciech Samek, Alexander Binder, and Klaus-Robert Müller: Multiple Kernel Learning for Brain-Computer Interfacing Proceedings of 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 7048-51, 2013 [bibtex] [pdf]* [url] *corrected manuscript

14.	Wojciech Samek, Klaus-Robert Müller, Motoaki Kawanabe, and Carmen Vidaurre: Brain-Computer Interfacing in Discriminative and Stationary Subspaces Proceedings of 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2873-76, 2012 [bibtex] [pdf] [url]

13.	Wojciech Samek, Alexander Binder, and Motoaki Kawanabe: Multi-task Learning via Non-sparse Multiple Kernel Learning Computer Analysis of Images and Patterns, Lecture Notes in Computer Science, 6854:335-42, Springer-Verlag, 2011 [bibtex] [pdf] [url]

12.	Wojciech Samek, Motoaki Kawanabe, and Carmen Vidaurre: Group-wise Stationary Subspace Analysis - A Novel Method for Studying Non-Stationarities Proceedings of 5th International Brain-Computer Interface Conference, 16-20, Verlag der TU Graz, 2011 [bibtex] [pdf]

11.	Motoaki Kawanabe, Wojciech Samek, Paul von Bünau, and Frank C. Meinecke: An Information Geometrical View of Stationary Subspace Analysis Artificial Neural Networks and Machine Learning ICANN 2011, Lecture Notes in Computer Science, 6792:397-404, Springer-Verlag, 2011 [bibtex] [pdf] [url]

10.

Wojciech Wojcikiewicz, Carmen Vidaurre, and Motoaki Kawanabe:
Improving Classification Performance of BCIs by using Stationary Common Spatial Patterns and Unsupervised Bias Adaptation
Hybrid Artificial Intelligent Systems, Lecture Notes in Computer Science, 6679:34-41, Springer-Verlag, 2011
[bibtex] [pdf] [url]

Wojciech Wojcikiewicz, Carmen Vidaurre, and Motoaki Kawanabe:
Stationary Common Spatial Patterns: Towards Robust Classification of Non-Stationary EEG Signals
Proceedings of 36th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 577-80, 2011
[bibtex] [pdf] [url] [video]

8.	Alexander Binder, Wojciech Wojcikiewicz, Christina Müller, and Motoaki Kawanabe: A Hybrid Supervised-Unsupervised Visual Vocabulary Algorithm for Concept Recognition Computer Vision ACCV 2010, Lecture Notes in Computer Science, 6494:95-108, Springer-Verlag, 2011 [bibtex] [pdf] [url]

7.	Alexander Binder, Wojciech Samek, Marius Kloft, Christina Müller, Klaus-Robert Müller, and Motoaki Kawanabe: The Joint Submission of the TU Berlin and Fraunhofer FIRST (TUBFI) to the ImageCLEF2011 Photo Annotation Task CLEF (Notebook Papers/Labs/Workshop), 1-9, 2011 [bibtex] [pdf] [url]

6.	Motoaki Kawanabe, Alexander Binder, Christina Müller, and Wojciech Wojcikiewicz: Multi-modal Visual Concept Classification of Images via Markov Random Walk over Tags Proceedings of the IEEE Workshop on Applications of Computer Vision (WACV), 396-401, 2011 [bibtex] [pdf] [url]

Alexander Binder, Shinichi Nakajima, Marius Kloft, Christina Müller, Wojciech Samek, Ulf Brefeld, Klaus-Robert Müller, and Motoaki Kawanabe:
On the Benefits and the Limits of Lp-norm Multiple Kernel Learning In Image Classification
Proceedings of the ICCV'11 Workshop on Kernels and Distances for Computer Vision, 1-3, 2011
[bibtex] [pdf]

4.	Wojciech Wojcikiewicz, Alexander Binder, and Motoaki Kawanabe: Shrinking Large Visual Vocabularies using Multi-label Agglomerative Information Bottleneck Proceedings of the 17th IEEE International Conference on Image Processing (ICIP), 3849-52, 2010 [bibtex] [pdf] [url]

3.	Wojciech Wojcikiewicz, Alexander Binder, and Motoaki Kawanabe: Enhancing Image Classification with Class-Wise Clustered Vocabularies Proceedings of the 20th International Conference on Pattern Recognition (ICPR), 1060-63, 2010 [bibtex] [pdf] [url]

Liam Pedersen, Marc Allan, Vinh To, Hans Utz, Wojciech Wojcikiewicz, and Christophe Chautems:
High Speed Lunar Navigation for Crewed and Remotely Piloted Vehicles
Proceedings of the 10th International Symposium on Artificial Intelligence, Robotics and Automation in Space (i-SAIRAS), 1-8, 2010
[bibtex] [pdf]

Shinichi Nakajima, Alexander Binder, Christina Müller, Wojciech Wojcikiewicz, Marius Kloft, Ulf Brefeld, Klaus-Robert Müller, and Motoaki Kawanabe:
Multiple Kernel Learning for Object Classification
Proceedings of the 12th Workshop on Information-based Induction Sciences (IBIS), 1-8, 2009
[bibtex] [pdf]

Datasets

2.	Patrick Wagner, Nils Strodthoff, Ralf-Dieter Bousseljot, Wojciech Samek, and Tobias Schaeffter: PTB-XL, A Large Publicly Available Electrocardiography Dataset PhysioNet, 2020 [bibtex] [url]

1.	Fabian Horst, Sebastian Lapuschkin, Wojciech Samek, Klaus-Robert Müller, and Wolfgang I. Schöllhorn: A Public Dataset of Overground Walking Kinetics and Full-Body Kinematics in Healthy Individuals Mendeley Data Repository, 2018 [bibtex] [url]

Theses

1.	Wojciech Samek: On Robust Spatial Filtering of EEG in Nonstationary Environments PhD Thesis, Technische Universität Berlin, 2014 [bibtex] [pdf] [url]

Activities

Editor

Senior Editor

Associate Editor

Special Issue Editor

Area Chair

Reviewer

Journals

IEEE Transactions on Pattern Analysis & Machine Intelligence

SIAM Journal on Mathematics of Data Science

ACM Transactions on Privacy and Security

IEEE Transactions on Biomedical Engineering

IEEE Transactions on Neural Networks and Learning Systems

IEEE Transactions on Neural Systems and Rehabilitation Engineering

IEEE Journal of Biomedical and Health Informatics

International Journal of Multimedia Information Retrieval

Computational Intelligence and Neuroscience

Biomedical Signal Processing and Control

IEEE Transactions on Cognitive and Developmental Systems

Conferences / TPC Member

Organizer

Workshops & Special Sessions

Tutorials

Invited Talks

Understanding & Validating LLMs with Explanations
AI Forum 2024, Berlin, Germany, 2024.

Explainable AI
Konferencja Władz Uczelnianych Matematyki i Informatyki, Krakow, Poland, 2024.

Explainable AI for LLMs
ML in PL Conference, Warsaw, Poland, 2024.

Wyjaśnialna sztuczna inteligencja: od metod do nowych spostrzeżeń
Inauguration Lecture at Warsaw University of Technology, Warsaw, Poland, 2024.

Concept-Level Explainable AI
Dagstuhl Seminar 24372 "Explainable AI for Sequential Decision Making", Schloss Dagstuhl, Germany, 2024.

From Feature Attributions to Next-Generation Explainable AI
11th International School on Deep Learning (DeepLearn), Porto, Portugal, 2024.

Towards Next-Generation Explainable AI
DEI Open Day at University of Porto, Porto, Portugal, 2024.

Explainable AI for LLMs
Nokia Bell Labs "Responsible AI Seminar Series", virtual talk, 2024.

Explainable AI for LLMs
24th International Conference on Artificial Intelligence and Soft Computing, Zakopane, Poland, 2024.

Explainable AI in the era of Large Language Models
AI for Good Global Summit, Geneva, Switzerland, 2024.

XAI-Based Model Debugging
Explainable AI Course at Warsaw University of Technology, Warsaw, Poland, 2024.

Explainable AI 2.0: From Heatmaps to Human-Understandable and Actionable Explanations
Machine Teaching for Human (MT4H) Workshop, Valencia, Spain, 2024.

Human-Centered Explainable AI
DFG Graduate School BIOQIC Seminars, Berlin, Germany, 2024.

Human-Centered Explainable AI
Machine Learning Seminars at Poznan University of Techology, Poznan, Poland, 2023.

New Opportunities and New Challenges Resulting from the AI Revolution
University of Warsaw, Warsaw, Poland, 2023.

From Local Explanations to Global Understanding
Explainable AI Course at Warsaw University of Technology, Warsaw, Poland, 2023.

Explainability 2.0 in the Era of Generative AI
AI Forum 2023, Berlin, Germany, 2023.

Concept-Level Explainable AI
SPAA & Dependability@Siemens, Nürnberg, Germany, 2023.

Explainable and Robust Machine Learning
Japanese-American-German Frontiers of Science Symposium, Dresden, Germany, 2023.

The New ISO/IEC Standard for Neural Network Coding
ECML PKDD'23 Workshop on "Simplification, Compression, Efficiency and Frugality for Artificial intelligence", Torino, Italy, 2023.

From Black-Box Models to Human-Understandable Explainable AI
Bayreuth Summer School of Philosophy and Computer Science, Bayreuth, Germany, 2023.

Accessing the Hidden Space of Models with Explainable AI
The Human Centric AI Seminars Series, virtual talk, 2023.

Concept-Level Explainable AI
IEEE CVPR'23 Workshop "Safe Artificial Intelligence for All Domains", Vancouver, Canada, 2023.

Concept-Level Explainable AI
Polish Conference on Artificial Intelligence, Lodz, Poland, 2023.

AI Liability, Machine Learning Research and Explainable AI
AI liability in the EU and the US: stifling or securing innovation, Berlin, Germany, 2023.

Machine Learning with Little Data
4th Meeting of Digital Pathology, San Servolo, Italy, 2023.

Concept-Level Explainable AI
Workshop on "Explainability in Machine Learning", Tübingen, Germany, 2023.

Concept-Level Explainable AI
WhiteBox Milestone Conference, Darmstadt, Germany, 2023.

Concept-Level Explainable AI
Workshop "Explainable AI for the Sciences: Towards Novel Insights", IPAM, 2023.

Concept-Level Explainable AI
3rd International Workshop on Auditing AI-Systems, Berlin, Germany, 2022.

Next-Generation Explainable AI
Max Planck School of Cognition Academy, Berlin, Germany, 2022.

Explainable Machine Learning for the Sciences
Symposium of the German National Academy of Sciences - Leopoldina, Halle, Germany, 2022.

Human-Machine Interactions Through Explanations
3rd ERCIM-JST Workshop, INRIA Rocquencourt, France, 2022.

Towards Human-Understandable XAI
Seminar Uni Augsburg, online event, 2022.

Global-Local XAI with Concept Relevance Propagation
2022 Workshop on Self-Supervised Learning for Signal Decoding, Aalborg, Denmark, 2022.

Concept-Level Explainable AI
Explainable AI for Wireless Communications, online event, 2022.

Explainable AI: Concepts, Methods and Applications
DeepLearn 2022 Summer 6th International Gran Canaria School on Deep Learning, Gran Canaria, Spain, 2022.

From Attribution Maps to Concept-Level Explainable AI
Pioneer Centre for AI Talk, Copenhagen, Denmark, 2022.

Towards Communication-Efficient and Personalized Federated Learning
2021 IEEE SPS Cycle 2 School on Networked Federated Learning: Theory, Algorithms and Applications, online event, 2022.

Explainable AI: Concepts, Methods and Recent Developments
Seminar on Bio-Inspired Artificial Neural Networks, online event, 2022.

Beyond Visualization: Using XAI for Better Models
Huawei Strategy and Technology Workshop, online event, 2021.

XXAI: eXtending XAI towards Actionable Interpretability
IEEE CVPR Workshop on "Interpretable Machine Learning for Computer Vision", online event, 2021.

Explainable AI: Concepts, Methods and Applications
2nd Eddy Cross Disciplinary Symposium, online event, 2021.

Recent Advances in Explainable AI
HEIBRIDS Lecture Series, online event, 2021.

Recent Advances in Federated Learning for Communication
ITU AI/ML in 5G Challenge, online event, 2020.

DeepCABAC: A Universal Compression Algorithm for Deep Neural Networks
Workshop on Energy Efficient Machine Learning and Cognitive Computing, online event, 2020.

Recent Advances in XAI
ACM CIKM'20 Workshop on "Advances in Machine Learning and Interpretable AI", Galway, Ireland, 2020.

From Local to Global Interpretations of DNNs
Workshop on Auditing AI-Systems: From Basics to Applications, Berlin, Germany, 2020.

Extending Explainable AI Beyond Deep Classifiers
MICCAI'20 Workshop on "Interpretability of Machine Intelligence in Medical Image Computing", Lima, Peru, 2020.

A Universal Compression Algorithm for Deep Neural Networks
AI for Good Global Summit 2020, Geneva, Switzerland, 2020.

Robust and Communication-Efficient Federated Learning
Workshop "Sensor AI", Berlin, Germany, 2020.

Explaining the Decisions of Deep Neural Networks and Beyond
Oberwolfach Workshop on "Statistics meets Machine Learning", Oberwolfach, Germany, 2020.

Explainable AI
FUTURAS IN RES Conference, Berlin, Germany, 2019.

Interpretable & Transparent Deep Learning
OpTecBB Workshop on Machine learning in Optical Analytics, Berlin, Germany, 2019.

Neuronale Netzwerke beim Denken beobachten
Nationales Digital Health Symposium, Berlin, Germany, 2019.

Federated Learning and its Applications in Communications
AI for 5G & Beyond Day, Geneva, Switzerland, 2019.

Meta-Explanations, Interpretable Clustering & Other Recent Developments
ICCV 2019 Workshop on Interpretating and Explaining Visual AI Models, Seoul, Korea, 2019.

Compression of Deep Neural Networks
ITU Workshop on "The future of media", Geneva, Switzerland, 2019.

Explainability of Deep Learning
9th Nachwuchsakademie Medizintechnik, Berlin, Germany, 2019.

Explainable Artificial Intelligence - Methods, Applications & Recent Developments
Cross Domain Conference for Machine Learning and Knowledge Extraction, Canterbury, UK, 2019.

Interpreting Deep Neural Networks
ICIAM Mini-Symposium on "Theoretical Foundations of Deep Learning", Valencia, Spain, 2019.

Deep Understanding of Deep Models
Artificial Intelligence Methods in Cosmology Workshop, Ascona, Switzerland, 2019.

Towards Explainable Artificial Intelligence
5th Digital Future Science Match, Berlin, Germany, 2019.

KI in der Gesundheitsversorgung Teil 2: Data Knows Nothing (panel discussion)
DMEA - Connecting Digital Health, Berlin, Germany, 2019.

Deep Learning: Models, Applications & Challenges
Leopoldina Meeting "Digital Pathology on the Boarder to Molecular Imaging", Venice, Italy, 2019.

Convergence of Machine Learning and Communications
CIEMI Congress on Intelligent systems, San Jose, Costa Rica, 2019.

Opening the Black Box: Making Deep Learning Interpretable & Transparent
Applied Machine Learning Days, Lausanne, Switzerland, 2019.

Interpreting and Explaining Deep Neural Networks
École polytechnique fédérale de Lausanne (EPFL), Lausanne, Switzerland, 2019.

Interpretable Deep Learning
Physikalisch-Technische Bundesanstalt (PTB), Berlin, Germany, 2019.

Interpretable & Transparent Deep Learning
Northern Lights Deep Learning Workshop, Tromsø, Norway, 2019.

Transparent & Interpretable Deep Learning for Health
8th Machine Learning in Healthcare Meetup, Berlin, Germany, 2019.

Interpreting and Explaining Deep Neural Networks
Columbia University, New York City, USA, 2018.

Interpretable Deep Learning & its Applications in the Neurosciences
Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, 2018.

Transparent & Interpretable Deep Learning
The Arctic University of Norway UIT, Tromsø, Norway, 2018.

Towards Interpretable Deep Learning
Open Data Science Conference, London, UK, 2018.

Interpreting Deep Neural Networks by Explaining their Predictions
Stanford University, USA, 2018.

Interpreting and Explaining Deep Models in Computer Vision
IAPR Summer School on Machine and Visual Intelligence, Vico Equense, Italy, 2018. [Slides]

Interpretable, Efficient and Distributed Deep Learning
Fritz Haber Institute, Berlin, Germany, 2018.

Towards Explainable AI
HIIG Workshop "Understanding AI and us", Berlin, Germany, 2018.

Understanding Machine Learning
IoT Week - Making the Data Revolution Happen: Machine Learning to Exploit Big Data, Bilbao, Spain, 2018.

How to Make the Black Box of Neural Networks Transparent - The Path Towards Explainable AI
AI for Good Global Summit 2018, Geneva, Switzerland, 2018.

Efficient, Distributed and Interpretable Deep Learning
ITU Workshop on "Impact of AI on ICT Infrastructures", Xi'an, China, 2018.

Interpreting Deep Neural Networks and their Predictions
2nd IML Machine Learning Workshop, CERN, Geneva, Switzerland, 2018.

Opening the Black Box of Deep Learning
11th RDA Plenary - Industry Side Meeting "Towards a Flourishing Data Economy", Berlin, Germany, 2018.

Ein Blick in die Blackbox - Transparenz von Künstlicher Intelligenz
Forum Digital Transformation: The impacts of Artificial Intelligence on Economy, Berlin, Germany, 2018.

Komplexitätsreduzierte Algorithmen des Maschinellen Lernens
Fraunhofer-Symposium "Netzwert", Munich, Germany, 2018.

Making Deep Neural Networks Transparent
HAP Workshop | Big Data Science in Astroparticle Physics, Aachen, Germany, 2018.

Explaining Deep Neural Network Decisions
Deep Learning for Computational Biology Workshop, Berlin, Germany, 2018.

Efficient Deep Learning in Communications
ITU Workshop on "Machine Learning for 5G and beyond", Geneva, Switzerland, 2018.

Interpretable Machine Learning
Artificial Intelligence for Practitioners, Berlin, Germany, 2018.

Wie intelligent ist die Künstliche Intelligenz?
Hybrid Talks XXIX »Intelligenz«, Berlin, Germany, 2018. [Talk]

Explaining Neural Networks in the Wild
NIPS Workshop: Interpreting, Explaining and Visualizing Deep Learning - Now what ?, Long Beach, USA, 2017.

Towards Explainable Deep Learning
CoSIP Intense Course on Deep Learning, Berlin, Germany, 2017. [Slides] [Video]

Methods for Understanding How Deep Neural Networks Work
Embedded Vision Alliance Vision Industry and Technology Forum, Hamburg, Germany, 2017.

Interpretable Machine Learning
DTU Summer School on Advanced Topics in Machine Learning, Copenhagen, Denmark, 2017. [Slides1] [Slides2] [Slides3] [Slides4]

What can we Learn from Interpreting Deep Neural Networks?
Deep Learning Workshop, Berlin, Germany, 2017. [Video]

Interpretable Deep Learning
Quo Vadis, Berlin, Germany, 2016.

Effective Data Analytics
Big Data Excellence for Utilities Conference, Berlin, Germany, 2016.

Explaining Individual Deep Network Predictions and Measuring the Quality of these Explanations
NIPS Workshop: Feature Extraction Workshop: Modern Questions and Challenges, Montreal, Canada, 2015.

Divergence-based Spatial Filtering for Robust BCI
Germany-Japan Adaptive BCI Workshop, Kyoto, Japan, 2015.

The Machine Learning Way to Video Analysis and Compression
Trends in Video Analysis, Representation and Delivery Workshop, Aachen, Germany, 2015.

Videos


CVPR Tutorial (2021)	EMC2 Workshop (2020)




Int. Summer School on Deep Learning (2020)	Die Wahrheit über... KI (2020)




AI for Good Global Summit(2020)	Projekt Zukunft, Deutsche Welle (2019)



CVPR Tutorial, Part 1 (2018)	CVPR Tutorial, Part 2 (2018)



Invited Talk at AI for Good Summit (2018)	MADE in Germany, Deutsche Welle (2018)



Hybrid Talk XXIX "Intelligence" (2018)	Invited Talk at CERN (2018)


Talk at Deep Learning Workshop (2017)	CeBIT (2017)


Talk at CoSIP Deep Learning Course (2017)	Fraunhofer Mythbusting (2017)


Fraunhofer Mythbusting (2017)	Spotlight Talk, NIPS (2013)

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