Wojciech Samek
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Computer Science
Wojciech Samek's Degrees
- PhD Computer Science Technical University of Berlin
- Masters Computer Science Technical University of Berlin
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(Suggest an Edit or Addition)Wojciech Samek's Published Works
Number of citations in a given year to any of this author's works
Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author
Published Works
- On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation (2015) (2829)
- Methods for interpreting and understanding deep neural networks (2017) (1647)
- Explaining nonlinear classification decisions with deep Taylor decomposition (2015) (1036)
- Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models (2017) (880)
- Evaluating the Visualization of What a Deep Neural Network Has Learned (2015) (841)
- Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data (2019) (700)
- Unmasking Clever Hans predictors and assessing what machines really learn (2019) (667)
- Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment (2016) (633)
- Explainable AI: Interpreting, Explaining and Visualizing Deep Learning (2019) (603)
- Layer-Wise Relevance Propagation: An Overview (2019) (372)
- Clustered Federated Learning: Model-Agnostic Distributed Multitask Optimization Under Privacy Constraints (2019) (349)
- A Unifying Review of Deep and Shallow Anomaly Detection (2020) (338)
- Interpretable deep neural networks for single-trial EEG classification (2016) (308)
- Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications (2021) (298)
- Explaining Recurrent Neural Network Predictions in Sentiment Analysis (2017) (288)
- Layer-Wise Relevance Propagation for Neural Networks with Local Renormalization Layers (2016) (282)
- PTB-XL, a large publicly available electrocardiography dataset (2020) (268)
- iNNvestigate neural networks! (2018) (264)
- Towards Explainable Artificial Intelligence (2019) (260)
- "What is relevant in a text document?": An interpretable machine learning approach (2016) (251)
- Stationary common spatial patterns for brain–computer interfacing (2012) (218)
- A deep neural network for image quality assessment (2016) (185)
- Analyzing Classifiers: Fisher Vectors and Deep Neural Networks (2015) (180)
- Artificial Intelligence in Dentistry: Chances and Challenges (2020) (176)
- Transferring Subspaces Between Subjects in Brain--Computer Interfacing (2012) (165)
- Divergence-Based Framework for Common Spatial Patterns Algorithms (2014) (162)
- Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communication (2018) (138)
- Explainable artificial intelligence (2017) (132)
- Interpreting and Explaining Deep Neural Networks for Classification of Audio Signals (2018) (130)
- Layer-Wise Relevance Propagation for Deep Neural Network Architectures (2016) (123)
- Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL (2020) (112)
- Understanding and Comparing Deep Neural Networks for Age and Gender Classification (2017) (110)
- Learning the invisible: a hybrid deep learning-shearlet framework for limited angle computed tomography (2018) (110)
- Resolving challenges in deep learning-based analyses of histopathological images using explanation methods (2019) (102)
- UDSMProt: universal deep sequence models for protein classification (2019) (100)
- The LRP Toolbox for Artificial Neural Networks (2016) (100)
- Explaining Predictions of Non-Linear Classifiers in NLP (2016) (98)
- Explainable ai – preface (2019) (96)
- Detection of Face Morphing Attacks by Deep Learning (2017) (96)
- Pruning by Explaining: A Novel Criterion for Deep Neural Network Pruning (2019) (94)
- Towards Best Practice in Explaining Neural Network Decisions with LRP (2019) (92)
- Explaining the unique nature of individual gait patterns with deep learning (2018) (91)
- Learning From More Than One Data Source: Data Fusion Techniques for Sensorimotor Rhythm-Based Brain–Computer Interfaces (2015) (82)
- Multivariate Machine Learning Methods for Fusing Multimodal Functional Neuroimaging Data (2015) (77)
- Toward Interpretable Machine Learning: Transparent Deep Neural Networks and Beyond (2020) (73)
- Evaluating Recurrent Neural Network Explanations (2019) (64)
- Analyzing Neuroimaging Data Through Recurrent Deep Learning Models (2018) (60)
- Asymptotically unbiased estimation of physical observables with neural samplers. (2020) (59)
- On the Byzantine Robustness of Clustered Federated Learning (2020) (59)
- Explainable AI Methods - A Brief Overview (2020) (56)
- DeepCABAC: A Universal Compression Algorithm for Deep Neural Networks (2019) (55)
- A Recurrent Convolutional Neural Network Approach for Sensorless Force Estimation in Robotic Surgery (2018) (53)
- What is Unique in Individual Gait Patterns? Understanding and Interpreting Deep Learning in Gait Analysis (2018) (51)
- Information fusion as an integrative cross-cutting enabler to achieve robust, explainable, and trustworthy medical artificial intelligence (2021) (51)
- Explaining and Interpreting LSTMs (2019) (51)
- Robust Spatial Filtering with Beta Divergence (2013) (47)
- Compact and Computationally Efficient Representation of Deep Neural Networks (2018) (45)
- From Clustering to Cluster Explanations via Neural Networks (2019) (44)
- Finding and removing Clever Hans: Using explanation methods to debug and improve deep models (2019) (41)
- Enhanced Machine Learning Techniques for Early HARQ Feedback Prediction in 5G (2018) (40)
- Explanation-Guided Training for Cross-Domain Few-Shot Classification (2020) (40)
- Interpreting the Predictions of Complex ML Models by Layer-wise Relevance Propagation (2016) (37)
- CLEVR-XAI: A benchmark dataset for the ground truth evaluation of neural network explanations (2021) (36)
- Brain-Computer Interfacing for multimedia quality assessment (2016) (32)
- Neural network based intra prediction for video coding (2018) (30)
- Identifying Individual Facial Expressions by Deconstructing a Neural Network (2016) (30)
- FedAUX: Leveraging Unlabeled Auxiliary Data in Federated Learning (2021) (30)
- Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations (2022) (29)
- Accurate and robust neural networks for face morphing attack detection (2020) (27)
- Software for Dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAy (2021) (27)
- Accurate and Robust Neural Networks for Security Related Applications Exampled by Face Morphing Attacks (2018) (27)
- Assessing Perceived Image Quality Using Steady-State Visual Evoked Potentials and Spatio-Spectral Decomposition (2018) (27)
- The Joint Submission of the TU Berlin and Fraunhofer FIRST (TUBFI) to the ImageCLEF2011 Photo Annotation Task (2011) (27)
- Interpretable human action recognition in compressed domain (2017) (26)
- Brain-computer interfacing in discriminative and stationary subspaces (2012) (26)
- Risk estimation of SARS-CoV-2 transmission from bluetooth low energy measurements (2020) (25)
- Objective quality assessment of stereoscopic images with vertical disparity using EEG (2017) (24)
- Robust Common Spatial Filters with a Maxmin Approach (2014) (24)
- Neural network-based full-reference image quality assessment (2016) (23)
- Multi-Kernel Prediction Networks for Denoising of Burst Images (2019) (22)
- Multiscale temporal neural dynamics predict performance in a complex sensorimotor task (2016) (22)
- Robustifying models against adversarial attacks by Langevin dynamics (2018) (22)
- Enhanced representation and multi-task learning for image annotation (2013) (22)
- Understanding Integrated Gradients with SmoothTaylor for Deep Neural Network Attribution (2020) (21)
- Localizing bicoherence from EEG and MEG (2018) (21)
- Controlling explanatory heatmap resolution and semantics via decomposition depth (2016) (21)
- Interpretable LSTMs For Whole-Brain Neuroimaging Analyses (2018) (21)
- Deep Transfer Learning For Whole-Brain fMRI Analyses (2019) (20)
- Overview of the Neural Network Compression and Representation (NNR) Standard (2022) (20)
- Hybrid video object tracking in H.265/HEVC video streams (2016) (20)
- Entropy-Constrained Training of Deep Neural Networks (2018) (19)
- Understanding Patch-Based Learning of Video Data by Explaining Predictions (2019) (19)
- The Convergence of Machine Learning and Communications (2017) (19)
- Brain–computer interfacing under distraction: an evaluation study (2016) (19)
- Bringing BCI into everyday life: Motor imagery in a pseudo realistic environment (2015) (18)
- DRAU: Dual Recurrent Attention Units for Visual Question Answering (2019) (18)
- Estimation of distortion sensitivity for visual quality prediction using a convolutional neural network (2019) (17)
- EEG source space analysis of the supervised factor analytic approach for the classification of multi-directional arm movement (17)
- Analyzing ImageNet with Spectral Relevance Analysis: Towards ImageNet un-Hans'ed (2019) (17)
- Communication-Efficient Federated Distillation (2020) (16)
- DeepCABAC: Context-adaptive binary arithmetic coding for deep neural network compression (2019) (16)
- Achieving Generalizable Robustness of Deep Neural Networks by Stability Training (2019) (15)
- Dithered backprop: A sparse and quantized backpropagation algorithm for more efficient deep neural network training (2020) (15)
- Group-wise Stationary Subspace Analysis-A novel method for studying non-stationarities (2011) (15)
- Robust common spatial patterns by minimum divergence covariance estimator (2014) (14)
- An Information Geometrical View of Stationary Subspace Analysis (2011) (14)
- Machine Learning for Health: Algorithm Auditing & Quality Control (2021) (14)
- Investigating effects of different artefact types on motor imagery BCI (2015) (14)
- CFD: Communication-Efficient Federated Distillation via Soft-Label Quantization and Delta Coding (2021) (13)
- On the Explanation of Machine Learning Predictions in Clinical Gait Analysis (2019) (13)
- Explain and improve: LRP-inference fine-tuning for image captioning models (2020) (12)
- From "Where" to "What": Towards Human-Understandable Explanations through Concept Relevance Propagation (2022) (12)
- Insights from Classifying Visual Concepts with Multiple Kernel Learning (2011) (12)
- On robust parameter estimation in brain–computer interfacing (2017) (12)
- Multi-task Learning via Non-sparse Multiple Kernel Learning (2011) (12)
- Deep Taylor Decomposition of Neural Networks (2016) (12)
- Estimation of Interaction Forces in Robotic Surgery using a Semi-Supervised Deep Neural Network Model (2018) (12)
- Towards Ground Truth Evaluation of Visual Explanations (2020) (12)
- Robust common spatial patterns based on Bhattacharyya distance and Gamma divergence (2015) (11)
- Multiple Kernel Learning for Brain-Computer Interfacing (2013) (11)
- Wasserstein Stationary Subspace Analysis (2018) (11)
- Interval Neural Networks: Uncertainty Scores (2020) (10)
- Toward Explainable AI for Regression Models (2021) (10)
- Exploring text datasets by visualizing relevant words (2017) (10)
- Toward Explainable Artificial Intelligence for Regression Models: A methodological perspective (2022) (10)
- Counterstrike: Defending Deep Learning Architectures Against Adversarial Samples by Langevin Dynamics with Supervised Denoising Autoencoder (2018) (10)
- Learning with explainable trees (2020) (9)
- Learning Sparse & Ternary Neural Networks with Entropy-Constrained Trained Ternarization (EC2T) (2020) (9)
- Information geometry meets BCI spatial filtering using divergences (2014) (9)
- USMPep: universal sequence models for major histocompatibility complex binding affinity prediction (2019) (9)
- A public dataset of overground walking kinetics and full-body kinematics in healthy adult individuals (2019) (8)
- On the Understanding and Interpretation of Machine Learning Predictions in Clinical Gait Analysis Using Explainable Artificial Intelligence (2019) (8)
- Machine learning methods of the Berlin brain-computer interface (2015) (8)
- Dual Recurrent Attention Units for Visual Question Answering (2018) (8)
- Explaining the Predictions of Unsupervised Learning Models (2020) (8)
- Tackling noise, artifacts and nonstationarity in BCI with robust divergences (2015) (8)
- Deepcabac: Plug & Play Compression of Neural Network Weights and Weight Updates (2020) (8)
- A perceptually relevant shearlet-based adaptation of the PSNR (2017) (8)
- Full-Reference Image Quality Assessment Using Neural Networks (2016) (7)
- Shearlet-based reduced reference image quality assessment (2016) (7)
- Analyzing and Validating Neural Networks Predictions (2016) (7)
- Explaining Machine Learning Models for Clinical Gait Analysis (2021) (7)
- MixMOOD: A systematic approach to class distribution mismatch in semi-supervised learning using deep dataset dissimilarity measures (2020) (7)
- ECQx: Explainability-Driven Quantization for Low-Bit and Sparse DNNs (2020) (7)
- Estimating Position & Velocity in 3D Space from Monocular Video Sequences Using a Deep Neural Network (2017) (7)
- FantastIC4: A Hardware-Software Co-Design Approach for Efficiently Running 4Bit-Compact Multilayer Perceptrons (2020) (7)
- Neural Network-Based Estimation of Distortion Sensitivity for Image Quality Prediction (2018) (7)
- Evaluating deep transfer learning for whole-brain cognitive decoding (2021) (6)
- On the Robustness of Pretraining and Self-Supervision for a Deep Learning-based Analysis of Diabetic Retinopathy (2021) (6)
- Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review (2022) (6)
- Beyond Explaining: Opportunities and Challenges of XAI-Based Model Improvement (2022) (6)
- On robust spatial filtering of EEG in nonstationary environments (2016) (6)
- Trends and Advancements in Deep Neural Network Communication (2020) (6)
- Machine Learning for Early HARQ Feedback Prediction in 5G (2018) (6)
- Dependent Scalar Quantization For Neural Network Compression (2020) (5)
- Viewport Forecasting in 360° Virtual Reality Videos with Machine Learning (2019) (5)
- Detecting failure modes in image reconstructions with interval neural network uncertainty (2021) (5)
- Risk estimation of SARS-CoV-2 transmission from bluetooth low energy measurements. (2020) (5)
- Information Theory Applications in Signal Processing (2019) (5)
- Machine Learning for Visual Concept Recognition and Ranking for Images (2014) (5)
- xxAI - Beyond Explainable Artificial Intelligence (2020) (5)
- Towards Trustworthy AI in Dentistry (2022) (5)
- On the Stimulation Frequency in SSVEP-based Image Quality Assessment (2018) (5)
- Understanding Patch-Based Learning by Explaining Predictions (2018) (5)
- Towards the Interpretability of Deep Learning Models for Human Neuroimaging (2021) (5)
- Federated Learning in Dentistry: Chances and Challenges (2022) (4)
- Towards the interpretability of deep learning models for multi-modal neuroimaging: Finding structural changes of the ageing brain (2022) (4)
- Discovering topics in text datasets by visualizing relevant words (2017) (4)
- On the robustness of action recognition methods in compressed and pixel domain (2016) (4)
- Understanding Image Captioning Models beyond Visualizing Attention (2020) (4)
- Comment on "Solving Statistical Mechanics Using VANs": Introducing saVANt - VANs Enhanced by Importance and MCMC Sampling (2019) (4)
- Encoder Optimizations For The NNR Standard On Neural Network Compression (2021) (4)
- XAI for Analyzing and Unlearning Spurious Correlations in ImageNet (2020) (3)
- Reward-Based 1-bit Compressed Federated Distillation on Blockchain (2021) (3)
- Explain and Improve: Cross-Domain Few-Shot-Learning Using Explanations (2020) (3)
- Defense Against Adversarial Attacks by Langevin Dynamics (2019) (3)
- Dataset Similarity to Assess Semisupervised Learning Under Distribution Mismatch Between the Labeled and Unlabeled Datasets (2023) (3)
- Quality assessment of image patches distorted by image compression using crowdsourcing (2016) (3)
- New definitions of human lymphoid and follicular cell entities in lymphatic tissue by machine learning (2022) (2)
- Benign Examples: Imperceptible Changes Can Enhance Image Translation Performance (2020) (2)
- Asymptotically Unbiased Generative Neural Sampling (2019) (2)
- Sensor Artificial Intelligence and its Application to Space Systems - A White Paper (2020) (2)
- Shortcomings of Top-Down Randomization-Based Sanity Checks for Evaluations of Deep Neural Network Explanations (2022) (2)
- Optimizing Explanations by Network Canonization and Hyperparameter Search (2022) (2)
- Alternative CSP approaches for multimodal distributed BCI data (2016) (2)
- Object Boundary Detection and Classification with Image-Level Labels (2016) (2)
- Quality perception of advanced multimedia systems (2019) (2)
- Causes of Outcome Learning: A causal inference-inspired machine learning approach to disentangling common combinations of potential causes of a health outcome (2020) (2)
- Black-Box Decision based Adversarial Attack with Symmetric $\alpha$-stable Distribution (2019) (1)
- Data Models for Dataset Drift Controls in Machine Learning With Images (2022) (1)
- Explain to Not Forget: Defending Against Catastrophic Forgetting with XAI (2022) (1)
- PatClArC: Using Pattern Concept Activation Vectors for Noise-Robust Model Debugging (2022) (1)
- Sharing hash codes for multiple purposes (2016) (1)
- ECQ$^{\text{x}}$: Explainability-Driven Quantization for Low-Bit and Sparse DNNs (2021) (1)
- Langevin Cooling for Domain Translation (2020) (1)
- USMPep: universal sequence models for major histocompatibility complex binding affinity prediction (2020) (1)
- LRP Toolbox for Artificial Neural Networks 1.2.0 – Manual (2016) (1)
- Black-Box Decision based Adversarial Attack with Symmetric α-stable Distribution (2019) (1)
- Interval Neural Networks as Instability Detectors for Image Reconstructions (2020) (1)
- FedAUXfdp: Differentially Private One-Shot Federated Distillation (2022) (1)
- Reveal to Revise: An Explainable AI Life Cycle for Iterative Bias Correction of Deep Models (2023) (1)
- Explaining the Decisions of Convolutional and Recurrent Neural Networks (2021) (1)
- Revealing Hidden Context Bias in Segmentation and Object Detection through Concept-specific Explanations (2022) (1)
- Adaptive Differential Filters for Fast and Communication-Efficient Federated Learning (2022) (1)
- Explainable AI for Time Series via Virtual Inspection Layers (2023) (1)
- Transferring Information Between Neural Networks (2018) (1)
- Measuring the Quality of 3D Visualizations using EEG: a Time-frequency Approach (2017) (1)
- Active multi-task learning with uncertainty weighted loss for coronary calcium scoring. (2022) (1)
- C V ] 16 D ec 2 01 1 Insights from Classifying Visual Concepts with Multiple Kern l Learning (2021) (0)
- Sharing hash codes for multiple purposes (2018) (0)
- Quality assessment of 3D visualizations with vertical disparity: An ERP approach (2017) (0)
- Unmasking Clever Hans predictors and assessing what machines really learn (2019) (0)
- Robust Spatial Filtering with Beta Divergence Supplemental Material (2013) (0)
- DeepLight: A Structured Framework For The Analysis of Neuroimaging Data Through Recurrent Deep Learning Models (2019) (0)
- Deep Learning for Whole-Brain Cognitive Decoding (2022) (0)
- Towards Auditable AI Systems Current status and future directions based on the workshop “ Auditing AI-Systems : From Basics to Applications (2021) (0)
- supp1-3129371.pdf (0)
- How to iNNvestigate neural networks' predictions! (2018) (0)
- To pretrain or not? A systematic analysis of the benefits of pretraining in diabetic retinopathy (2022) (0)
- The Meta-Evaluation Problem in Explainable AI: Identifying Reliable Estimators with MetaQuantus (2023) (0)
- Zero Shot Learning for Semantic Boundary Detection - How Far Can We Get? (2016) (0)
- Explaining machine learning models for age classification in human gait analysis (2022) (0)
- Explaining the unique nature of individual gait patterns with deep learning (2019) (0)
- PTB-XL, a large publicly available electrocardiography dataset (2020) (0)
- Resolving challenges in deep learning-based analyses of histopathological images using explanation methods (2020) (0)
- How to iNNvestigate neural network’s predictions! (2018) (0)
- Statistics meets Machine Learning 5 Abstracts Explaining the decisions of deep neural networks and beyond (2020) (0)
- History Dependent Significance Coding for Incremental Neural Network Compression (2022) (0)
- A Privacy Preserving System for Movie Recommendations using Federated Learning (2023) (0)
- Artificial Intelligence in Dental Diagnostics: Chances and challenges (2020) (0)
- XAI-based Comparison of Input Representations for Audio Event Classification (2023) (0)
- Langevin Cooling for Unsupervised Domain Translation. (2022) (0)
- Rotation Invariant Clustering of 3D Cell Nuclei Shapes * (2019) (0)
- Explaining automated gender classification of human gait (2020) (0)
- Stationary Linear Discriminant Analysis-Classifying Non-Stationary Features in Brain-Computer Interfacing (2011) (0)
- Causes of Outcome Learning: a causal inference-inspired machine learning approach to disentangling common combinations of potential causes of a health outcome. (2022) (0)
- Acknowledgement to Reviewers 2013 (2013) (0)
- Host-pathogen co-adaptation shapes susceptibility to infection with Mycobacterium tuberculosis (2022) (0)
- Learning with explainable trees (2020) (0)
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