List of works - Wojciech Samek

"What is relevant in a text document?": An interpretable machine learning approach

scientific article

Analyzing Classifiers: Fisher Vectors and Deep Neural Networks

scientific article published in June 2016

Analyzing Neuroimaging Data Through Recurrent Deep Learning Models

scientific article published on 10 December 2019

Artificial Intelligence in Dentistry: Chances and Challenges

scientific article published on 21 April 2020

Asymptotically unbiased estimation of physical observables with neural samplers

scientific article published on 01 February 2020

Brain-computer interfacing in discriminative and stationary subspaces ⬇️

scientific article published on January 1, 2012

Brain-computer interfacing under distraction: an evaluation study

scientific article

CLEVR-XAI: A benchmark dataset for the ground truth evaluation of neural network explanations

scientific article published in 2022

Causes of Outcome Learning: a causal inference-inspired machine learning approach to disentangling common combinations of potential causes of a health outcome

scientific article published in 2022

Clustered Federated Learning: Model-Agnostic Distributed Multitask Optimization Under Privacy Constraints

scientific article published on 24 August 2020

Compact and Computationally Efficient Representation of Deep Neural Networks

scientific article published on 29 May 2019

Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL

scientific article published on 09 September 2020

Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment

scientific article

DeepCABAC: A Universal Compression Algorithm for Deep Neural Networks

scientific article published on 28 January 2020

Divergence-based framework for common spatial patterns algorithms

scientific article

Evaluating Recurrent Neural Network Explanations

scientific article published in 2019

Evaluating the Visualization of What a Deep Neural Network Has Learned

scientific article

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

scholarly article

Explaining Predictions of Non-Linear Classifiers in NLP

scientific article published in 2016

Explaining Recurrent Neural Network Predictions in Sentiment Analysis ⬇️

scientific article published on 4 August 2017

Explaining and Interpreting LSTMs

scientific article published in 2019

Explaining nonlinear classification decisions with deep Taylor decomposition

article

Explaining the unique nature of individual gait patterns with deep learning

scientific article published on 20 February 2019

FantastIC4: A Hardware-Software Co-Design Approach for Efficiently Running 4Bit-Compact Multilayer Perceptrons

scientific article published on 25 May 2021

Information Theory Applications in Signal Processing

scientific article published on 03 July 2019

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

scientific article published in 2022

Insights from Classifying Visual Concepts with Multiple Kernel Learning ⬇️

scientific article published on August 24, 2012

Interpretable deep neural networks for single-trial EEG classification

scientific article published on 13 October 2016

Interpretable human action recognition in compressed domain

scientific article published in March 2017

Investigating effects of different artefact types on motor imagery BCI.

scientific article published on August 2015

Layer-Wise Relevance Propagation for Neural Networks with Local Renormalization Layers

scientific article published in 2016

Layer-Wise Relevance Propagation: An Overview

scientific article published in 2019

Localizing bicoherence from EEG and MEG.

scientific article published on 5 February 2018

Methods for interpreting and understanding deep neural networks

scientific article published in February 2018

Multiple kernel learning for brain-computer interfacing

scientific article published in January 2013

Multiscale temporal neural dynamics predict performance in a complex sensorimotor task

scientific article published on 8 July 2016

Objective quality assessment of stereoscopic images with vertical disparity using EEG.

scientific article published on 25 May 2017

On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation

scientific article

On robust parameter estimation in brain-computer interfacing

scientific article

PTB-XL, a large publicly available electrocardiography dataset

scientific article published on 25 May 2020

Pruning by explaining: A novel criterion for deep neural network pruning

scientific article published on 22 February 2021

Quality assessment of 3D visualizations with vertical disparity: An ERP approach

scientific article published in July 2017

Resolving challenges in deep learning-based analyses of histopathological images using explanation methods

scientific article published on 14 April 2020

Risk estimation of SARS-CoV-2 transmission from bluetooth low energy measurements

scientific article published on 06 October 2020

Robust Spatial Filtering with Beta Divergence ⬇️

Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data

scientific article published on 01 November 2019

Robust common spatial filters with a max-min approach

scientific article published on 8 November 2013

Rotation Invariant Clustering of 3D Cell Nuclei Shapes

scientific article published on 01 July 2019

Stationary common spatial patterns for brain–computer interfacing

scientific article published on February 20, 2012

The LRP Toolbox for Artificial Neural Networks ⬇️

academic journal article

Towards Explainable Artificial Intelligence

scientific article published in 2019

Transferring subspaces between subjects in brain--computer interfacing

scientific article published on 20 March 2013

Understanding Patch-Based Learning of Video Data by Explaining Predictions

scientific article published in 2019

Unmasking Clever Hans predictors and assessing what machines really learn

scientific article published on 11 March 2019

List of works by Wojciech Samek

"What is relevant in a text document?": An interpretable machine learning approach

Analyzing Classifiers: Fisher Vectors and Deep Neural Networks

Analyzing Neuroimaging Data Through Recurrent Deep Learning Models

Artificial Intelligence in Dentistry: Chances and Challenges

Asymptotically unbiased estimation of physical observables with neural samplers

Brain-computer interfacing in discriminative and stationary subspaces ⬇️

Brain-computer interfacing under distraction: an evaluation study

CLEVR-XAI: A benchmark dataset for the ground truth evaluation of neural network explanations

Causes of Outcome Learning: a causal inference-inspired machine learning approach to disentangling common combinations of potential causes of a health outcome

Clustered Federated Learning: Model-Agnostic Distributed Multitask Optimization Under Privacy Constraints

Compact and Computationally Efficient Representation of Deep Neural Networks

Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL

Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment

DeepCABAC: A Universal Compression Algorithm for Deep Neural Networks

Divergence-based framework for common spatial patterns algorithms

Evaluating Recurrent Neural Network Explanations

Evaluating the Visualization of What a Deep Neural Network Has Learned

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

Explaining Predictions of Non-Linear Classifiers in NLP

Explaining Recurrent Neural Network Predictions in Sentiment Analysis ⬇️

Explaining and Interpreting LSTMs

Explaining nonlinear classification decisions with deep Taylor decomposition

Explaining the unique nature of individual gait patterns with deep learning

FantastIC4: A Hardware-Software Co-Design Approach for Efficiently Running 4Bit-Compact Multilayer Perceptrons

Information Theory Applications in Signal Processing

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

Insights from Classifying Visual Concepts with Multiple Kernel Learning ⬇️

Interpretable deep neural networks for single-trial EEG classification

Interpretable human action recognition in compressed domain

Investigating effects of different artefact types on motor imagery BCI.

Layer-Wise Relevance Propagation for Neural Networks with Local Renormalization Layers

Layer-Wise Relevance Propagation: An Overview

Localizing bicoherence from EEG and MEG.

Methods for interpreting and understanding deep neural networks

Multiple kernel learning for brain-computer interfacing

Multiscale temporal neural dynamics predict performance in a complex sensorimotor task

Objective quality assessment of stereoscopic images with vertical disparity using EEG.

On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation

On robust parameter estimation in brain-computer interfacing

PTB-XL, a large publicly available electrocardiography dataset

Pruning by explaining: A novel criterion for deep neural network pruning

Quality assessment of 3D visualizations with vertical disparity: An ERP approach

Resolving challenges in deep learning-based analyses of histopathological images using explanation methods

Risk estimation of SARS-CoV-2 transmission from bluetooth low energy measurements

Robust Spatial Filtering with Beta Divergence ⬇️

Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data

Robust common spatial filters with a max-min approach

Rotation Invariant Clustering of 3D Cell Nuclei Shapes

Stationary common spatial patterns for brain–computer interfacing

The LRP Toolbox for Artificial Neural Networks ⬇️

Towards Explainable Artificial Intelligence

Transferring subspaces between subjects in brain--computer interfacing

Understanding Patch-Based Learning of Video Data by Explaining Predictions

Unmasking Clever Hans predictors and assessing what machines really learn