Wojciech Samek

Orcid: 0000-0002-6283-3265

According to our database1, Wojciech Samek authored at least 207 papers between 2011 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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On csauthors.net:

Bibliography

2024
From Clustering to Cluster Explanations via Neural Networks.
IEEE Trans. Neural Networks Learn. Syst., February, 2024

AudioMNIST: Exploring Explainable Artificial Intelligence for audio analysis on a simple benchmark.
J. Frankl. Inst., 2024

Explain to Question not to Justify.
CoRR, 2024

DualView: Data Attribution from the Dual Perspective.
CoRR, 2024

AttnLRP: Attention-Aware Layer-wise Relevance Propagation for Transformers.
CoRR, 2024

Explaining Predictive Uncertainty by Exposing Second-Order Effects.
CoRR, 2024

From Hope to Safety: Unlearning Biases of Deep Models via Gradient Penalization in Latent Space.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
Langevin Cooling for Unsupervised Domain Translation.
IEEE Trans. Neural Networks Learn. Syst., October, 2023

FedAUX: Leveraging Unlabeled Auxiliary Data in Federated Learning.
IEEE Trans. Neural Networks Learn. Syst., September, 2023

From attribution maps to human-understandable explanations through Concept Relevance Propagation.
Nat. Mac. Intell., September, 2023

Evaluating deep transfer learning for whole-brain cognitive decoding.
J. Frankl. Inst., September, 2023

Dataset Similarity to Assess Semisupervised Learning Under Distribution Mismatch Between the Labeled and Unlabeled Datasets.
IEEE Trans. Artif. Intell., April, 2023

Beyond explaining: Opportunities and challenges of XAI-based model improvement.
Inf. Fusion, April, 2023

Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review.
IEEE Internet Things J., February, 2023

Data Models for Dataset Drift Controls in Machine Learning With Optical Images.
Trans. Mach. Learn. Res., 2023

Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond.
J. Mach. Learn. Res., 2023

Understanding the (Extra-)Ordinary: Validating Deep Model Decisions with Prototypical Concept-based Explanations.
CoRR, 2023

DMLR: Data-centric Machine Learning Research - Past, Present and Future.
CoRR, 2023

Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions.
CoRR, 2023

Generative Fractional Diffusion Models.
CoRR, 2023

Layer-wise Feedback Propagation.
CoRR, 2023

From Hope to Safety: Unlearning Biases of Deep Models by Enforcing the Right Reasons in Latent Space.
CoRR, 2023

XAI-based Comparison of Input Representations for Audio Event Classification.
CoRR, 2023

Explainable AI for Time Series via Virtual Inspection Layers.
CoRR, 2023

A Privacy Preserving System for Movie Recommendations using Federated Learning.
CoRR, 2023

The Meta-Evaluation Problem in Explainable AI: Identifying Reliable Estimators with MetaQuantus.
CoRR, 2023

DiffInfinite: Large Mask-Image Synthesis via Parallel Random Patch Diffusion in Histopathology.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Reveal to Revise: An Explainable AI Life Cycle for Iterative Bias Correction of Deep Models.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2023, 2023

Human-Centered Evaluation of XAI Methods.
Proceedings of the IEEE International Conference on Data Mining, 2023

Optimizing Explanations by Network Canonization and Hyperparameter Search.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

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, 2023

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, 2023

XAI-based Comparison of Audio Event Classifiers with different Input Representations.
Proceedings of the 20th International Conference on Content-based Multimedia Indexing, 2023

2022
CFD: Communication-Efficient Federated Distillation via Soft-Label Quantization and Delta Coding.
IEEE Trans. Netw. Sci. Eng., 2022

Overview of the Neural Network Compression and Representation (NNR) Standard.
IEEE Trans. Circuits Syst. Video Technol., 2022

Toward Explainable Artificial Intelligence for Regression Models: A methodological perspective.
IEEE Signal Process. Mag., 2022

Towards the interpretability of deep learning models for multi-modal neuroimaging: Finding structural changes of the ageing brain.
NeuroImage, 2022

Explain and improve: LRP-inference fine-tuning for image captioning models.
Inf. Fusion, 2022

Information fusion as an integrative cross-cutting enabler to achieve robust, explainable, and trustworthy medical artificial intelligence.
Inf. Fusion, 2022

CLEVR-XAI: A benchmark dataset for the ground truth evaluation of neural network explanations.
Inf. Fusion, 2022

Finding and removing Clever Hans: Using explanation methods to debug and improve deep models.
Inf. Fusion, 2022

Explaining Machine Learning Models for Clinical Gait Analysis.
ACM Trans. Comput. Heal., 2022

Explaining machine learning models for age classification in human gait analysis.
CoRR, 2022

Explaining automated gender classification of human gait.
CoRR, 2022

Data Models for Dataset Drift Controls in Machine Learning With Images.
CoRR, 2022

From "Where" to "What": Towards Human-Understandable Explanations through Concept Relevance Propagation.
CoRR, 2022

Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations.
CoRR, 2022

PatClArC: Using Pattern Concept Activation Vectors for Noise-Robust Model Debugging.
CoRR, 2022

FedAUXfdp: Differentially Private One-Shot Federated Distillation.
Proceedings of the Trustworthy Federated Learning - First International Workshop, 2022

History Dependent Significance Coding for Incremental Neural Network Compression.
Proceedings of the 2022 IEEE International Conference on Image Processing, 2022

Adaptive Differential Filters for Fast and Communication-Efficient Federated Learning.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022

Explain to Not Forget: Defending Against Catastrophic Forgetting with XAI.
Proceedings of the Machine Learning and Knowledge Extraction, 2022

Deep Learning for Whole-Brain Cognitive Decoding.
Proceedings of the 10th International Winter Conference on Brain-Computer Interface, 2022

2021
Clustered Federated Learning: Model-Agnostic Distributed Multitask Optimization Under Privacy Constraints.
IEEE Trans. Neural Networks Learn. Syst., 2021

Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL.
IEEE J. Biomed. Health Informatics, 2021

Pruning by explaining: A novel criterion for deep neural network pruning.
Pattern Recognit., 2021

Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications.
Proc. IEEE, 2021

A Unifying Review of Deep and Shallow Anomaly Detection.
Proc. IEEE, 2021

FantastIC4: A Hardware-Software Co-Design Approach for Efficiently Running 4Bit-Compact Multilayer Perceptrons.
IEEE Open J. Circuits Syst., 2021

Robustifying models against adversarial attacks by Langevin dynamics.
Neural Networks, 2021

Machine Learning for Health: Algorithm Auditing & Quality Control.
J. Medical Syst., 2021

Toward Explainable AI for Regression Models.
CoRR, 2021

Reward-Based 1-bit Compressed Federated Distillation on Blockchain.
CoRR, 2021

On the Robustness of Pretraining and Self-Supervision for a Deep Learning-based Analysis of Diabetic Retinopathy.
CoRR, 2021

Software for Dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAy.
CoRR, 2021

Detecting failure modes in image reconstructions with interval neural network uncertainty.
Int. J. Comput. Assist. Radiol. Surg., 2021

Encoder Optimizations For The NNR Standard On Neural Network Compression.
Proceedings of the 2021 IEEE International Conference on Image Processing, 2021

Interval Neural Networks as Instability Detectors for Image Reconstructions.
Proceedings of the Bildverarbeitung für die Medizin 2021, 2021

2020
Compact and Computationally Efficient Representation of Deep Neural Networks.
IEEE Trans. Neural Networks Learn. Syst., 2020

Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data.
IEEE Trans. Neural Networks Learn. Syst., 2020

Risk estimation of SARS-CoV-2 transmission from bluetooth low energy measurements.
npj Digit. Medicine, 2020

Learning with explainable trees.
Nat. Mach. Intell., 2020

DeepCABAC: A Universal Compression Algorithm for Deep Neural Networks.
IEEE J. Sel. Top. Signal Process., 2020

Accurate and robust neural networks for face morphing attack detection.
J. Inf. Secur. Appl., 2020

Communication-Efficient Federated Distillation.
CoRR, 2020

Langevin Cooling for Domain Translation.
CoRR, 2020

Sensor Artificial Intelligence and its Application to Space Systems - A White Paper.
CoRR, 2020

MixMOOD: A systematic approach to class distribution mismatch in semi-supervised learning using deep dataset dissimilarity measures.
CoRR, 2020

Interval Neural Networks: Uncertainty Scores.
CoRR, 2020

Toward Interpretable Machine Learning: Transparent Deep Neural Networks and Beyond.
CoRR, 2020

Towards Ground Truth Evaluation of Visual Explanations.
CoRR, 2020

Trends and Advancements in Deep Neural Network Communication.
CoRR, 2020

Understanding Image Captioning Models beyond Visualizing Attention.
CoRR, 2020

USMPep: universal sequence models for major histocompatibility complex binding affinity prediction.
BMC Bioinform., 2020

UDSMProt: universal deep sequence models for protein classification.
Bioinform., 2020

Towards Best Practice in Explaining Neural Network Decisions with LRP.
Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

Explanation-Guided Training for Cross-Domain Few-Shot Classification.
Proceedings of the 25th International Conference on Pattern Recognition, 2020

Understanding Integrated Gradients with SmoothTaylor for Deep Neural Network Attribution.
Proceedings of the 25th International Conference on Pattern Recognition, 2020

Explaining the Predictions of Unsupervised Learning Models.
Proceedings of the xxAI - Beyond Explainable AI, 2020

Explainable AI Methods - A Brief Overview.
Proceedings of the xxAI - Beyond Explainable AI, 2020

xxAI - Beyond Explainable Artificial Intelligence.
Proceedings of the xxAI - Beyond Explainable AI, 2020

ECQ<sup> x</sup>: Explainability-Driven Quantization for Low-Bit and Sparse DNNs.
Proceedings of the xxAI - Beyond Explainable AI, 2020

Deepcabac: Plug & Play Compression of Neural Network Weights and Weight Updates.
Proceedings of the IEEE International Conference on Image Processing, 2020

Dependent Scalar Quantization For Neural Network Compression.
Proceedings of the IEEE International Conference on Image Processing, 2020

On the Byzantine Robustness of Clustered Federated Learning.
Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

Dithered backprop: A sparse and quantized backpropagation algorithm for more efficient deep neural network training.
Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

Learning Sparse & Ternary Neural Networks with Entropy-Constrained Trained Ternarization (EC2T).
Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

Benign Examples: Imperceptible Changes Can Enhance Image Translation Performance.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019
Towards Explainable Artificial Intelligence.
Proceedings of the Explainable AI: Interpreting, 2019

Layer-Wise Relevance Propagation: An Overview.
Proceedings of the Explainable AI: Interpreting, 2019

Explaining and Interpreting LSTMs.
Proceedings of the Explainable AI: Interpreting, 2019

Understanding Patch-Based Learning of Video Data by Explaining Predictions.
Proceedings of the Explainable AI: Interpreting, 2019

Enhanced Machine Learning Techniques for Early HARQ Feedback Prediction in 5G.
IEEE J. Sel. Areas Commun., 2019

iNNvestigate Neural Networks!
J. Mach. Learn. Res., 2019

Information Theory Applications in Signal Processing.
Entropy, 2019

Quality perception of advanced multimedia systems.
Digit. Signal Process., 2019

Estimation of distortion sensitivity for visual quality prediction using a convolutional neural network.
Digit. Signal Process., 2019

DRAU: Dual Recurrent Attention Units for Visual Question Answering.
Comput. Vis. Image Underst., 2019

Analyzing ImageNet with Spectral Relevance Analysis: Towards ImageNet un-Hans'ed.
CoRR, 2019

Pruning by Explaining: A Novel Criterion for Deep Neural Network Pruning.
CoRR, 2019

On the Understanding and Interpretation of Machine Learning Predictions in Clinical Gait Analysis Using Explainable Artificial Intelligence.
CoRR, 2019

Asymptotically Unbiased Generative Neural Sampling.
CoRR, 2019

Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints.
CoRR, 2019

Explaining and Interpreting LSTMs.
CoRR, 2019

Towards Explainable Artificial Intelligence.
CoRR, 2019

Resolving challenges in deep learning-based analyses of histopathological images using explanation methods.
CoRR, 2019

DeepCABAC: A Universal Compression Algorithm for Deep Neural Networks.
CoRR, 2019

From Clustering to Cluster Explanations via Neural Networks.
CoRR, 2019

DeepCABAC: Context-adaptive binary arithmetic coding for deep neural network compression.
CoRR, 2019

Comment on "Solving Statistical Mechanics Using VANs": Introducing saVANt - VANs Enhanced by Importance and MCMC Sampling.
CoRR, 2019

Robust and Communication-Efficient Federated Learning from Non-IID Data.
CoRR, 2019

Unmasking Clever Hans Predictors and Assessing What Machines Really Learn.
CoRR, 2019

A recurrent convolutional neural network approach for sensorless force estimation in robotic surgery.
Biomed. Signal Process. Control., 2019

Deep Transfer Learning for Whole-Brain FMRI Analyses.
Proceedings of the OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging, 2019

Entropy-Constrained Training of Deep Neural Networks.
Proceedings of the International Joint Conference on Neural Networks, 2019

Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communication.
Proceedings of the International Joint Conference on Neural Networks, 2019

Multi-Kernel Prediction Networks for Denoising of Burst Images.
Proceedings of the 2019 IEEE International Conference on Image Processing, 2019

Black-Box Decision based Adversarial Attack with Symmetric α-stable Distribution.
Proceedings of the 27th European Signal Processing Conference, 2019

Rotation Invariant Clustering of 3D Cell Nuclei Shapes.
Proceedings of the 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2019

Achieving Generalizable Robustness of Deep Neural Networks by Stability Training.
Proceedings of the Pattern Recognition, 2019

Evaluating Recurrent Neural Network Explanations.
Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, 2019

Viewport Forecasting in 360° Virtual Reality Videos with Machine Learning.
Proceedings of the 2019 IEEE International Conference on Artificial Intelligence and Virtual Reality, 2019

2018
Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment.
IEEE Trans. Image Process., 2018

Assessing Perceived Image Quality Using Steady-State Visual Evoked Potentials and Spatio-Spectral Decomposition.
IEEE Trans. Circuits Syst. Video Technol., 2018

Localizing bicoherence from EEG and MEG.
NeuroImage, 2018

Wasserstein Stationary Subspace Analysis.
IEEE J. Sel. Top. Signal Process., 2018

Methods for interpreting and understanding deep neural networks.
Digit. Signal Process., 2018

Learning The Invisible: A Hybrid Deep Learning-Shearlet Framework for Limited Angle Computed Tomography.
CoRR, 2018

Interpretable LSTMs For Whole-Brain Neuroimaging Analyses.
CoRR, 2018

What is Unique in Individual Gait Patterns? Understanding and Interpreting Deep Learning in Gait Analysis.
CoRR, 2018

Interpreting and Explaining Deep Neural Networks for Classification of Audio Signals.
CoRR, 2018

Understanding Patch-Based Learning by Explaining Predictions.
CoRR, 2018

Accurate and Robust Neural Networks for Security Related Applications Exampled by Face Morphing Attacks.
CoRR, 2018

Counterstrike: Defending Deep Learning Architectures Against Adversarial Samples by Langevin Dynamics with Supervised Denoising Autoencoder.
CoRR, 2018

Dual Recurrent Attention Units for Visual Question Answering.
CoRR, 2018

On the Stimulation Frequency in SSVEP-based Image Quality Assessment.
Proceedings of the Tenth International Conference on Quality of Multimedia Experience, 2018

Estimation of Interaction Forces in Robotic Surgery using a Semi-Supervised Deep Neural Network Model.
Proceedings of the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2018

Neural Network-Based Estimation of Distortion Sensitivity for Image Quality Prediction.
Proceedings of the 2018 IEEE International Conference on Image Processing, 2018

Transferring Information Between Neural Networks.
Proceedings of the 2018 IEEE International Conference on Acoustics, 2018

Machine Learning for Early HARQ Feedback Prediction in 5G.
Proceedings of the IEEE Globecom Workshops, 2018

2017
Evaluating the Visualization of What a Deep Neural Network Has Learned.
IEEE Trans. Neural Networks Learn. Syst., 2017

Explaining nonlinear classification decisions with deep Taylor decomposition.
Pattern Recognit., 2017

The Convergence of Machine Learning and Communications.
CoRR, 2017

Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models.
CoRR, 2017

Discovering topics in text datasets by visualizing relevant words.
CoRR, 2017

Exploring text datasets by visualizing relevant words.
CoRR, 2017

Explaining Recurrent Neural Network Predictions in Sentiment Analysis.
Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, 2017

Detection of Face Morphing Attacks by Deep Learning.
Proceedings of the Digital Forensics and Watermarking - 16th International Workshop, 2017

A perceptually relevant shearlet-based adaptation of the PSNR.
Proceedings of the 2017 IEEE International Conference on Image Processing, 2017

Understanding and Comparing Deep Neural Networks for Age and Gender Classification.
Proceedings of the 2017 IEEE International Conference on Computer Vision Workshops, 2017

Estimating Position & Velocity in 3D Space from Monocular Video Sequences Using a Deep Neural Network.
Proceedings of the 2017 IEEE International Conference on Computer Vision Workshops, 2017

Interpretable human action recognition in compressed domain.
Proceedings of the 2017 IEEE International Conference on Acoustics, 2017

Measuring the Quality of 3D Visualizations using EEG: a Time-frequency Approach.
Proceedings of the From Vision to Reality, 2017

Quality assessment of 3D visualizations with vertical disparity: An ERP approach.
Proceedings of the 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2017

Object Boundary Detection and Classification with Image-Level Labels.
Proceedings of the Pattern Recognition - 39th German Conference, 2017

2016
Multiscale temporal neural dynamics predict performance in a complex sensorimotor task.
NeuroImage, 2016

The LRP Toolbox for Artificial Neural Networks.
J. Mach. Learn. Res., 2016

On robust spatial filtering of EEG in nonstationary environments.
it Inf. Technol., 2016

Interpretable Deep Neural Networks for Single-Trial EEG Classification.
CoRR, 2016

Interpreting the Predictions of Complex ML Models by Layer-wise Relevance Propagation.
CoRR, 2016

Sharing Hash Codes for Multiple Purposes.
CoRR, 2016

Zero Shot Learning for Semantic Boundary Detection - How Far Can We Get?
CoRR, 2016

"What is Relevant in a Text Document?": An Interpretable Machine Learning Approach.
CoRR, 2016

Alternative CSP approaches for multimodal distributed BCI data.
Proceedings of the 2016 IEEE International Conference on Systems, Man, and Cybernetics, 2016

Brain-Computer Interfacing for multimedia quality assessment.
Proceedings of the 2016 IEEE International Conference on Systems, Man, and Cybernetics, 2016

Explaining Predictions of Non-Linear Classifiers in NLP.
Proceedings of the 1st Workshop on Representation Learning for NLP, 2016

Neural network-based full-reference image quality assessment.
Proceedings of the 2016 Picture Coding Symposium, 2016

Hybrid video object tracking in H.265/HEVC video streams.
Proceedings of the 18th IEEE International Workshop on Multimedia Signal Processing, 2016

Quality assessment of image patches distorted by image compression using crowdsourcing.
Proceedings of the IEEE International Conference on Multimedia and Expo, 2016

A deep neural network for image quality assessment.
Proceedings of the 2016 IEEE International Conference on Image Processing, 2016

Shearlet-based reduced reference image quality assessment.
Proceedings of the 2016 IEEE International Conference on Image Processing, 2016

Controlling explanatory heatmap resolution and semantics via decomposition depth.
Proceedings of the 2016 IEEE International Conference on Image Processing, 2016

Layer-Wise Relevance Propagation for Neural Networks with Local Renormalization Layers.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2016, 2016

On the robustness of action recognition methods in compressed and pixel domain.
Proceedings of the 6th European Workshop on Visual Information Processing, 2016

Identifying Individual Facial Expressions by Deconstructing a Neural Network.
Proceedings of the Pattern Recognition - 38th German Conference, 2016

Analyzing Classifiers: Fisher Vectors and Deep Neural Networks.
Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016

2015
Learning From More Than One Data Source: Data Fusion Techniques for Sensorimotor Rhythm-Based Brain-Computer Interfaces.
Proc. IEEE, 2015

Multivariate Machine Learning Methods for Fusing Multimodal Functional Neuroimaging Data.
Proc. IEEE, 2015

Bringing BCI into everyday life: Motor imagery in a pseudo realistic environment.
Proceedings of the 7th International IEEE/EMBS Conference on Neural Engineering, 2015

Tackling noise, artifacts and nonstationarity in BCI with robust divergences.
Proceedings of the 23rd European Signal Processing Conference, 2015

Investigating effects of different artefact types on motor imagery BCI.
Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2015

Robust common spatial patterns based on Bhattacharyya distance and Gamma divergence.
Proceedings of the 3rd International Winter Conference on Brain-Computer Interface, 2015

2014
Machine Learning for Visual Concept Recognition and Ranking for Images.
Proceedings of the Towards the Internet of Services: The THESEUS Research Program, 2014

On robust spatial filtering of EEG in nonstationary environments.
PhD thesis, 2014

Robust Common Spatial Filters with a Maxmin Approach.
Neural Comput., 2014

Robust common spatial patterns by minimum divergence covariance estimator.
Proceedings of the IEEE International Conference on Acoustics, 2014

Über die robuste räumliche Filterung von EEG in nichtstationären Umgebungen.
Proceedings of the Ausgezeichnete Informatikdissertationen 2014, 2014

Information geometry meets BCI spatial filtering using divergences.
Proceedings of the 2014 International Winter Workshop on Brain-Computer Interface, 2014

2013
Transferring Subspaces Between Subjects in Brain-Computer Interfacing.
IEEE Trans. Biomed. Eng., 2013

Enhanced representation and multi-task learning for image annotation.
Comput. Vis. Image Underst., 2013

Robust Spatial Filtering with Beta Divergence.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Multiple Kernel Learning for Brain-Computer Interfacing.
Proceedings of the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2013

2012
Brain-computer interfacing in discriminative and stationary subspaces.
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012

2011
Insights from Classifying Visual Concepts with Multiple Kernel Learning
CoRR, 2011

An Information Geometrical View of Stationary Subspace Analysis.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2011, 2011

The Joint Submission of the TU Berlin and Fraunhofer FIRST (TUBFI) to the ImageCLEF2011 Photo Annotation Task.
Proceedings of the CLEF 2011 Labs and Workshop, 2011

Multi-task Learning via Non-sparse Multiple Kernel Learning.
Proceedings of the Computer Analysis of Images and Patterns, 2011


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