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Methods for interpreting and understanding deep neural networks

Digital signal processing, 2018-02, Vol.73, p.1-15

2018

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A convolutional neural network for steady state visual evoked potential classification under ambulatory environment

PloS one, 2017-02, Vol.12 (2), p.e0172578-e0172578

2017

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A large scale screening study with a SMR-based BCI: Categorization of BCI users and differences in their SMR activity

PloS one, 2019-01, Vol.14 (1), p.e0207351-e0207351

2019

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Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology

Machine learning and knowledge extraction, 2021-06, Vol.3 (2), p.392-413

2021

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Stationary common spatial patterns for brain-computer interfacing

Journal of neural engineering, 2012-04, Vol.9 (2), p.026013-1-14

2012

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Predicting BCI subject performance using probabilistic spatio-temporal filters

PloS one, 2014-02, Vol.9 (2), p.e87056

2014

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Morphological and molecular breast cancer profiling through explainable machine learning

Nature machine intelligence, 2021-04, Vol.3 (4), p.355-366

2021

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True zero-training brain-computer interfacing--an online study

PloS one, 2014-07, Vol.9 (7), p.e102504-e102504

2014

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Explaining nonlinear classification decisions with deep Taylor decomposition

Pattern recognition, 2017-05, Vol.65, p.211-222

2017

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Improvement of Information Transfer Rates Using a Hybrid EEG-NIRS Brain-Computer Interface with a Short Trial Length: Offline and Pseudo-Online Analyses

Sensors (Basel, Switzerland), 2018-06, Vol.18 (6), p.1827

2018

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

IEEE transaction on neural networks and learning systems, 2021-08, Vol.32 (8), p.3710-3722

2021

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

Pattern recognition, 2021-07, Vol.115, p.107899, Article 107899

2021

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Assessing the Realizable Flexibility Potential of Electrochemical Processes

Industrial & engineering chemistry research, 2021-09, Vol.60 (37), p.13637-13660

2021

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Brain-Switches for Asynchronous Brain–Computer Interfaces: A Systematic Review

Electronics (Basel), 2020-03, Vol.9 (3), p.422

2020

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Towards explaining anomalies: A deep Taylor decomposition of one-class models

Pattern recognition, 2020-05, Vol.101, p.107198, Article 107198

2020

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Gold nanocubes – Direct comparison of synthesis approaches reveals the need for a microfluidic synthesis setup for a high reproducibility

Chemical engineering journal (Lausanne, Switzerland : 1996), 2016-03, Vol.288, p.432-440

2016

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Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications

Proceedings of the IEEE, 2021-03, Vol.109 (3), p.247-278

2021

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An efficient ERP-based brain-computer interface using random set presentation and face familiarity

PloS one, 2014-11, Vol.9 (11), p.e111157-e111157

2014

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

IEEE transaction on neural networks and learning systems, 2020-09, Vol.31 (9), p.3400-3413

2020

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Brain-computer interfacing under distraction: an evaluation study

Journal of neural engineering, 2016-10, Vol.13 (5), p.056012-056012

2016

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Evaluating the Visualization of What a Deep Neural Network Has Learned

IEEE transaction on neural networks and learning systems, 2017-11, Vol.28 (11), p.2660-2673

2017

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Towards robust explanations for deep neural networks

Pattern recognition, 2022-01, Vol.121, p.108194, Article 108194

2022

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Three-way analysis of spectrospatial electromyography data: classification and interpretation

PloS one, 2015-06, Vol.10 (6), p.e0127231-e0127231

2015

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A Unifying Review of Deep and Shallow Anomaly Detection

Proceedings of the IEEE, 2021-05, Vol.109 (5), p.756-795

2021

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Enhanced representation and multi-task learning for image annotation

Computer vision and image understanding, 2013-05, Vol.117 (5), p.466-478

2013

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Methods for interpreting and understanding deep neural networks

A convolutional neural network for steady state visual evoked potential classification under ambulatory environment

A large scale screening study with a SMR-based BCI: Categorization of BCI users and differences in their SMR activity

Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology

Stationary common spatial patterns for brain-computer interfacing

Predicting BCI subject performance using probabilistic spatio-temporal filters

Morphological and molecular breast cancer profiling through explainable machine learning

True zero-training brain-computer interfacing--an online study

Explaining nonlinear classification decisions with deep Taylor decomposition

Improvement of Information Transfer Rates Using a Hybrid EEG-NIRS Brain-Computer Interface with a Short Trial Length: Offline and Pseudo-Online Analyses

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

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

Assessing the Realizable Flexibility Potential of Electrochemical Processes

Brain-Switches for Asynchronous Brain–Computer Interfaces: A Systematic Review

Towards explaining anomalies: A deep Taylor decomposition of one-class models

Gold nanocubes – Direct comparison of synthesis approaches reveals the need for a microfluidic synthesis setup for a high reproducibility

Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications

An efficient ERP-based brain-computer interface using random set presentation and face familiarity

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

Brain-computer interfacing under distraction: an evaluation study

Evaluating the Visualization of What a Deep Neural Network Has Learned

Towards robust explanations for deep neural networks

Three-way analysis of spectrospatial electromyography data: classification and interpretation

A Unifying Review of Deep and Shallow Anomaly Detection

Enhanced representation and multi-task learning for image annotation

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