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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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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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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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Algorithmic Differentiation for Automated Modeling of Machine Learned Force Fields

The journal of physical chemistry letters, 2022-11, Vol.13 (43), p.10183-10189

2022

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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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A lower limb exoskeleton control system based on steady state visual evoked potentials

Journal of neural engineering, 2015-10, Vol.12 (5), p.056009-056009

2015

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Superparamagnetic Luminescent MOF@Fe3O4/SiO2 Composite Particles for Signal Augmentation by Magnetic Harvesting as Potential Water Detectors

ACS applied materials & interfaces, 2016-03, Vol.8 (8), p.5445-5452

2016

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Forecasting industrial aging processes with machine learning methods

Computers & chemical engineering, 2021-01, Vol.144, p.107123, Article 107123

2021

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Robustifying models against adversarial attacks by Langevin dynamics

Neural networks, 2021-05, Vol.137, p.1-17

2021

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N-ary decomposition for multi-class classification

Machine learning, 2019-05, Vol.108 (5), p.809-830

2019

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Preemptively pruning Clever-Hans strategies in deep neural networks

Information fusion, 2024-03, Vol.103, p.102094, Article 102094

2024

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Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space

The journal of physical chemistry letters, 2015-06, Vol.6 (12), p.2326-2331

2015

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A mathematical model for the two-learners problem

Journal of neural engineering, 2017-06, Vol.14 (3), p.036005-036005

2017

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Learning domain invariant representations by joint Wasserstein distance minimization

Neural networks, 2023-10, Vol.167, p.233-243

2023

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Ensembles of adaptive spatial filters increase BCI performance: an online evaluation

Journal of neural engineering, 2016-08, Vol.13 (4), p.046003-046003

2016

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Concept for the Real-Time Monitoring of Molecular Configurations during Manipulation with a Scanning Probe Microscope

Journal of physical chemistry. C, 2023-07, Vol.127 (28), p.13817-13836

2023

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EEG-based BCI for the linear control of an upper-limb neuroprosthesis

Medical engineering & physics, 2016-11, Vol.38 (11), p.1195-1204

2016

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Evaluating deep transfer learning for whole-brain cognitive decoding

Journal of the Franklin Institute, 2023-09, Vol.360 (13), p.9754-9787

2023

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An explainable AI framework for robust and transparent data-driven wind turbine power curve models

Energy and AI, 2024-01, Vol.15, p.100328, Article 100328

2024

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Co-adaptive calibration to improve BCI efficiency

Journal of neural engineering, 2011-04, Vol.8 (2), p.025009-1-8

2011

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Smart Optical Composite Materials: Dispersions of Metal–Organic Framework@Superparamagnetic Microrods for Switchable Isotropic–Anisotropic Optical Properties

ACS nano, 2017-01, Vol.11 (1), p.779-787

2017

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Integrating dynamic stopping, transfer learning and language models in an adaptive zero-training ERP speller

Journal of neural engineering, 2014-06, Vol.11 (3), p.035005-035005

2014

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AudioMNIST: Exploring Explainable Artificial Intelligence for audio analysis on a simple benchmark

Journal of the Franklin Institute, 2024-01, Vol.361 (1), p.418-428

2024

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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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Estimation of distortion sensitivity for visual quality prediction using a convolutional neural network

Digital signal processing, 2019-08, Vol.91, p.54-65

2019

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Methods for interpreting and understanding deep neural networks

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

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

Algorithmic Differentiation for Automated Modeling of Machine Learned Force Fields

Towards robust explanations for deep neural networks

A lower limb exoskeleton control system based on steady state visual evoked potentials

Superparamagnetic Luminescent MOF@Fe3O4/SiO2 Composite Particles for Signal Augmentation by Magnetic Harvesting as Potential Water Detectors

Forecasting industrial aging processes with machine learning methods

Robustifying models against adversarial attacks by Langevin dynamics

N-ary decomposition for multi-class classification

Preemptively pruning Clever-Hans strategies in deep neural networks

Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space

A mathematical model for the two-learners problem

Learning domain invariant representations by joint Wasserstein distance minimization

Ensembles of adaptive spatial filters increase BCI performance: an online evaluation

Concept for the Real-Time Monitoring of Molecular Configurations during Manipulation with a Scanning Probe Microscope

EEG-based BCI for the linear control of an upper-limb neuroprosthesis

Evaluating deep transfer learning for whole-brain cognitive decoding

An explainable AI framework for robust and transparent data-driven wind turbine power curve models

Co-adaptive calibration to improve BCI efficiency

Smart Optical Composite Materials: Dispersions of Metal–Organic Framework@Superparamagnetic Microrods for Switchable Isotropic–Anisotropic Optical Properties

Integrating dynamic stopping, transfer learning and language models in an adaptive zero-training ERP speller

AudioMNIST: Exploring Explainable Artificial Intelligence for audio analysis on a simple benchmark

Explaining nonlinear classification decisions with deep Taylor decomposition

Estimation of distortion sensitivity for visual quality prediction using a convolutional neural network

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