Fraunhofer Heinrich Hertz Institute 
   Dr. Wojciech Samek
   Head of Machine Learning Group
Department of Video Coding & Analytics
   Fraunhofer Institute for Telecommunications
Heinrich Hertz Institute (HHI)
Einsteinufer 37
10587 Berlin
Germany 

Tel:  +49 30 31002-417
Fax: +49 30 31002-190

     
  [ Bio | Research | Teaching | Publications | Activities ]
     
   Short Bio 

   Wojciech Samek received the Diploma degree in computer science from Humboldt-Universität zu Berlin in 2010 and the Dr. rer. nat. degree with distinction from the Technische Universität Berlin in 2014 under the supervision of Klaus-Robert Müller. He worked as a researcher for the Machine Learning Group at TU Berlin, the Intelligent Data Analysis Group at Fraunhofer FIRST and the Automotive Services and Communication Technologies Group at Fraunhofer FOKUS and was responsible for various scientific as well as Industry-funded research projects, including a Strategic Japan-Germany Cooperative Program on Computational Neuroscience. In 2014 he founded the Machine Learning Group at Fraunhofer Heinrich Hertz Institute which he currently directs. He is associated with the Berlin Big Data Center (BBDC) and was Scholar of the Studienstiftung des deutschen Volkes and a Ph.D. Fellow at the Bernstein Center for Computational Neuroscience (BCCN) Berlin and the DFG Research Training Group "Sensory Computation in Neural Systems". From 2007 to 2008 he was visiting Heriot-Watt University and the University of Edinburgh, in 2009 he was with the Intelligent Robotics Group at NASA Ames Research Center, Mountain View, CA, and in 2012 and 2013 he had several short research stays in Japan and UK. His research interests include machine learning, signal processing, biomedical engineering and computer vision. 


   News 

    2017-03-24 Article about our work on interpretability in ZEIT ONLINE.  


   Events 

    2017-09-12 GCPR 2017 Tutorial on "Interpretable Machine Learning" in Basel, Switzerland
2017-03-20 Presentation 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. Our slides: [part1] [part2] [part3]
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 

   Deep Learning 
   Understanding Deep Classifiers
   Nonlinear methods such as Deep Neural Networks (DNNs) are the gold standard in many fields of machine learning. Although these approaches perform impressively well, they have a significant disadvantage, the lack of interpretability, limiting the applicability in practice. Our research focuses on the development of methods which allow one to understand and interpret the reasoning of these complex classifiers. See also www.heatmapping.org.
   Deep Video Analysis
   Recently, it was shown that deep architectures significantly outperform shallow representations on complex image classification tasks. Similar techniques can be applied to problems such as human action recognition or general video analysis. The focus of our research is to further advance deep learning techniques for this type of analysis and to find ways of integrating different modalities in this context.
      
   Applications of Deep Learning
   We develop and evaluate deep architectures for a variety of complex real-world tasks and data types, including images, videos, text, time series and sensor data. The main focus of our work lies on convolutional nets and deep autoencoders.
      
   Biomedical Signal Analysis 
   EEG-based Quality Assessment
   The quality of a video compression scheme can be measured in terms of mean squared pixel error. However, this mathematical quantity does not necessary reflect the perceived quality of a user. The focus of our research is the development of a direct measurement of video quality change perception using EEG.
   BCI & Spatial Filtering
   Spatial filtering is a crucial step in motor imagery BCI as it reduces dimensionality of the data while increasing its signal-to-noise ratio. The Common Spatial Patterns algorithm is a popular method to compute spatial filters. However, in practice this algorithm may perform poorly if data is nonstationary or contaminated by artifacts. The focus of this research is to develop robust spatial filtering algorithms by utilizing concepts from information geometry.
   Seizures, Criticality & Neuroscience
   About 1% of people worldwide have epilepsy. Unexpected seizures may significantly decrease the quality of life for patients and lead to severe injuries. The focus of our research is to develop methods for predicting seizures from intracranial EEG recordings and to help understanding seizure generation by identifying seizure specific features.
      
   Machine Learning 
   Divergence Methods
   Many machine learning algorithms can be cast into the framework of information geometry and formulated as divergence optimization problems. By using specific divergences and distribution models one can add additional properties such as robustness or nonlinearity to an algorithm. Information Geometry provides a theoretical foundation for this type of methods. Our research focuses on the development of a generic divergence framework incorporating many popular machine learning algorithms and the analysis of its advantages and limitations.
      
   Robustness & Nonstationarity
   Real-world data are often intrinsically nonstationary, i.e., the data distribution changes. This change is often not considered when developing machine learning algorithms. Our research focuses on the development of methods explicitly tackling the nonstationarity problem.
      

   Previous Research 

   Image Classification 
   Bag-of-Words Models
   In recent years bag-of-visual-words representations have gained increasing popularity in the field of image classification. In our research we focus on integration of various image features by non-sparce MKL, efficient codebook generation, multi-task learning and multi-modal visual concept classification based on visual features and user tags.
   Non-maximum suppression
   Scanning window object detectors typically produce a number of positive responses close by to the correct detection, and this leads to the need to have a further non-maximum suppression stage to thin out the multiple responses and to suppress spurious responses. Our research focuses on a principled approach to NMS by modelling the detections of a single object with a scale-sensitive Gaussian distribution and then describing the set of all detections with mixture of SSGs.
      
   Robotics 
   Autonomous Robot Navigation
   Fast and autonomous robot navigation in a challenging lunar-like environment is a hard problem. The environment must be sensed and processed very fast and the system must be very accurate as wrong decisions about whether a path is traversable or not can be fatal and result in mission failure. Our research focuses on the requirements analysis for safe navigation and the detection of occlussions from 3D LIDAR measurements.
   RoboCup
   In the four-legged league in RoboCup four autonomous robots are playing against other four autonomous robots. Our research focuses on the development of intelligent behaviour for the robot. This involves four domains, namely perception, modelling, behaviour and motion.
      

   Teaching 
   

Cognitive Algorithms (Lecture, TU Berlin, Summer 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)
Doktorandenseminar (Seminar, TU Berlin, Summer 2014)
Machine Learning for Biomedical Engineering (Seminar, TU Berlin, Summer 2014)
Doktorandenseminar (Seminar, TU Berlin, Winter 2013/14)
Doktorandenseminar (Seminar, TU Berlin, Summer 2013)
Doktorandenseminar (Seminar, TU Berlin, Winter 2012/13)
Doktorandenseminar (Seminar, TU Berlin, Summer 2012)
Doktorandenseminar (Seminar, TU Berlin, Winter 2011/12)
Doktorandenseminar (Seminar, TU Berlin, Summer 2011)
Theoretische Informatik I (Exercise Tutor, HU Berlin, Winter 2006/07)
Theoretische Informatik I (Exercise Tutor, HU Berlin, Winter 2005/06)


   Publications 
   

Preprints

4.Leila Arras, Franziska Horn, Grégoire Montavon, Klaus-Robert Müller, and Wojciech Samek:
"What is Relevant in a Text Document?": An Interpretable Machine Learning Approach
arXiv:1612.07843, 2016
[bibtex] [pdf] [url]

3.Sebastian Bosse, Dominique Maniry, Klaus-Robert Müller, Thomas Wiegand, and Wojciech Samek:
Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment
arXiv:1609.03219, 2016
[bibtex] [pdf] [url]

2.Wikor 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
arXiv:1609.03219, 2016
[bibtex] [pdf] [url]

1.Jing Yu Koh, Wojciech Samek, Klaus-Robert Müller, and Alexander Binder:
Zero Shot Learning for Semantic Boundary Detection - How Far Can We Get?
arXiv:1606.09187, 2016
[bibtex] [pdf] [url]


Books and Book Chapters

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

18.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, 2017
[bibtex] [pdf] [url]

17.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, 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.Irene Sturm, Sebastian Lapuschkin, Wojciech Samek, and Klaus-Robert Müller:
Interpretable Deep Neural Networks for Single-Trial EEG Classification
Journal of Neuroscience Methods, 274:141–145, 2016
[bibtex] [pdf] [url]

14.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, 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, 141:291–303, 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]

8.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

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), 2017
[bibtex] [pdf]

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 5th International Brain-Computer Interface Conference, Verlag der TU Graz, 2017
[bibtex] [pdf]

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), 2017
[bibtex] [pdf]

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 Workshop at the Conference on Neural Information Processing Systems (NIPS), 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 Workshop on Visualization for Deep Learning at International Conference on Machine Learning (ICML), 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 Workshop on Visualization for Deep Learning at International Conference on Machine Learning (ICML), 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] [url]

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 Workshop on Representation Learning for NLP at Association for Computational Linguistics Conference (ACL), 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 Arbabzadeh, 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, 6679:913-22, 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]

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 (52 spotlights out of 1420 submissions) ***
[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]

9.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, Prag, 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), 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]

5.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 1st IEEE Workshop on Kernels and Distances for Computer Vision (ICCV'11 workshop), 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]

2.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), 2010
[bibtex] [pdf]

1.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), 2009
[bibtex] [pdf]


Other Publications

1.Wojciech Samek:
On robust spatial filtering of EEG in nonstationary environments
PhD Thesis, Technische Universität Berlin, 2014
[bibtex] [pdf] [pdf (small)] [url]


   Activities 

   

Reviewer

Journals
Proceedings of IEEE
Neural Computation
Journal of Neural Engineering
Journal of Biomedical Engineering
Journal of Neuroscience Methods
IEEE Transactions on Biomedical Engineering
IEEE Transactions on Neural Networks and Learning Systems
IEEE Transactions on Neural Systems & Rehabilitation Engineering
IEEE Journal of Biomedical and Health Informatics
International Journal of Multimedia Information Retrieval
Computational Intelligence and Neuroscience
Computers in Biology and Medicine
Medical Engineering & Physics
Pattern Recognition Letters
Signal Processing Letters
Neurocomputing
Machine Learning
Image Communication
Biomedical Signal Processing & Control
Neural Computing and Applications
Healthcare Technology Letters
Review of Scientific Instruments
Brain-Computer Interfaces
Brain Topography
Sensors
Entropy


Conferences
Machine Learning for Signal Processing (MLSP)
IEEE Engineering in Medicine and Biology Society Conference (EMBC)
German Conference on Pattern Recognition (GCPR)
International Conference on Machine Learning (ICML)
Neural Information Processing Systems (NIPS)
International Joint Conference on Artificial Intelligence (IJCAI)
IEEE Global Conference on Signal and Information Processing (GlobalSP)
International Conference on Machine learning and Signal Processing (MALSIP)
European Signal Processing Conference (EUSIPCO)
International Conference on Signal Processing and Communication Systems (SPCS)
IEEE International Conference on Systems, Man, and Cybernetics (SMC)
International Conference on Artificial Intelligence and Statistics (AISTATS)


Books
Elsevier Science and Technology Book Publishing

   

Service to the Community

Tutorial on "Interpretable Machine Learning" at GCPR 2017.
Tutorial on "Methods for Interpreting and Understanding Deep Neural Networks" at ICASSP 2017.
Organizer of Workshop Interpretation and Visualization of Deep Neural Nets at ACCV 2016.
Organizer of Workshop Machine learning and interpretability at ICANN 2016.
Special Session on "Robust EEG signals processing towards practical Brain-Computer Interface design" at EUSIPCO 2015.
Program Committee Member at ICML Visualization Workshop 2016.
Program Committee Member for AISTATS 2017.
Program Committee Member at SMC 2017.
Program Committee Member at SPCS 2016, 2017.
Program Committee Member at Germany-Japan Adaptive BCI Workshop 2015.
Program Committee Member on symposium on "Signal Processing Challenges in Human Brain Connectomics" at IEEE GlobalSP 2015.
Program Committee Member for the Machine Learning Track of IJCAI 2015.

   

Invited Talks & Presentations

Quo Vadis, Apr 18-20th, 2016 in Berlin.
Feature Extraction Workshop: Modern Questions and Challenges, NIPS workshop, Dec 11th, 2015 in Montreal.

   

Supervision

Maximilian Kohlbrenner (Technische Universität Berlin, 2017)
Bachelor's Thesis: On the Stability of Neural Network Explanations.
Simon Wiedemann (Technische Universität Berlin, 2017)
Master's Thesis: Rate-Distortion Optimization of Deep Neural Networks.
Ashwin Nair (Technische Universität Chemnitz, 2016)
Master's Thesis: Compressed Domain Action Recognition with Neural Networks.
Jan Timo Meyer (Technische Universität Berlin, 2016)
Bachelor's Thesis: Compressed Domain Video Object Tracking with Spatio-Temporal Markov Random Fields.
Dominique Maniry (Technische Universität Berlin, 2016)
Master's Thesis: Image Quality Assessment with Deep Neural Networks.
Leila Arras (Technische Universität Berlin, 2015)
Bachelor's Thesis: Classification of Text Documents via a Convolutional Neural Network using pre-trained Word Embeddings.
Stephanie Brandl (Humboldt University Berlin, 2015)
Diploma Thesis: Divergence Based Spatial Filter Computation for Brain-Computer Interfacing.
Guilherme A. Zimeo Morais (Politecnico Di Milano, 2012)
Master's Thesis: One-Class Support Vector Machine for Outlier Detection in Brain-Computer Interface.
Duncan Blythe (BCCN Berlin, 2011)
Master's Thesis: Two Projection Pursuit Algorithms for Machine Learning under Non-Stationarity.