2020
|
Ghanbari, M; Ohler, U Deep neural networks for interpreting RNA-binding protein target preferences Journal Article Genome Research, 2020 Jan 28 , 2020. Links | BibTeX @article{Ghanbari2020,
title = { Deep neural networks for interpreting RNA-binding protein target preferences},
author = {M Ghanbari and U Ohler},
url = {https://www.ncbi.nlm.nih.gov/pubmed/31992613},
doi = {10.1101/gr.247494.118},
year = {2020},
date = {2020-01-07},
journal = {Genome Research},
volume = {2020 Jan 28},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
2019
|
Lalli, R; Howey, R; Wintergrün, D The dynamics of collaboration networks and the history of general relativity, 1925–1970 Journal Article Scientometrics , 2019. Links | BibTeX @article{Lalli2019,
title = {The dynamics of collaboration networks and the history of general relativity, 1925–1970},
author = {R Lalli and R Howey and D Wintergrün},
url = {https://doi.org/10.1007/s11192-019-03327-1.},
year = {2019},
date = {2019-12-16},
journal = {Scientometrics },
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Valleriani, M; Kräutli, F; Zamani, M; Tejedor, A; Sander, C; Vogl, M; Bertram, S; Funke, G; Kantz, H The Emergence of Epistemic Communities in the Sphaera Corpus Journal Article Journal of Historical Network Research, 3 (1), pp. 50-91, 2019. Links | BibTeX @article{val2019,
title = {The Emergence of Epistemic Communities in the Sphaera Corpus},
author = {M Valleriani and F Kräutli and M Zamani and A Tejedor and C Sander and M Vogl and S Bertram and G Funke and H Kantz},
doi = {https://doi.org/10.25517/jhnr.v3i1.63 },
year = {2019},
date = {2019-11-19},
journal = {Journal of Historical Network Research},
volume = {3},
number = {1},
pages = {50-91},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Vidaurre, C; Nolte, G; de Vries, IEJ; Gómez, M; Boonstra, TW; Müller, KR; Villringer, A; Nikulin, VV Canonical maximization of coherence: A novel tool for investigation of neuronal interactions between two datasets Journal Article NeuroImage, 201 , 2019. Links | BibTeX @article{Vidaurre2019,
title = {Canonical maximization of coherence: A novel tool for investigation of neuronal interactions between two datasets},
author = {C Vidaurre and G Nolte and IEJ de Vries and M Gómez and TW Boonstra and KR Müller and A Villringer and VV Nikulin},
url = {https://www.sciencedirect.com/science/article/abs/pii/S1053811919305907?dgcid=rss_sd_all},
year = {2019},
date = {2019-11-01},
journal = {NeuroImage},
volume = {201},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
von Lühmann, A; Boukouvalas, Z; Müller, KR; Adalı, T A new blind source separation framework for signal analysis and artifact rejection in functional Near-Infrared Spectroscopy Journal Article NeuroImage, 200 , pp. 72-88, 2019. Links | BibTeX @article{vonLuehmann2019,
title = {A new blind source separation framework for signal analysis and artifact rejection in functional Near-Infrared Spectroscopy},
author = {A von Lühmann and Z Boukouvalas and KR Müller and T Adalı
},
url = {https://www.sciencedirect.com/science/article/abs/pii/S1053811919305129
},
year = {2019},
date = {2019-10-15},
journal = {NeuroImage},
volume = {200},
pages = {72-88},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Vidaurre, C; Murguialday, AR; Haufe, S; Gómez, M; Müller, KR; Nikulin, VV Enhancing sensorimotor BCI performance with assistive afferent activity: An online evaluation Journal Article NeuroImage, 199 , pp. 375-386, 2019. Links | BibTeX @article{Vidaurre2019b,
title = {Enhancing sensorimotor BCI performance with assistive afferent activity: An online evaluation},
author = {C Vidaurre and AR Murguialday and S Haufe and M Gómez and KR Müller and VV Nikulin},
url = {https://www.sciencedirect.com/science/article/abs/pii/S1053811919304707?via%3Dihub
},
year = {2019},
date = {2019-10-01},
journal = {NeuroImage},
volume = {199},
pages = {375-386},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Sauceda, HE; Chmiela, S; Poltavsky, I; Müller, KR; Tkatchenko, A Construction of Machine Learned Force Fields with Quantum Chemical Accuracy: Applications and Chemical Insights Journal Article 2019. Links | BibTeX @article{Sauceda2019b,
title = {Construction of Machine Learned Force Fields with Quantum Chemical Accuracy: Applications and Chemical Insights},
author = {HE Sauceda and S Chmiela and I Poltavsky and KR Müller and A Tkatchenko
},
url = {https://arxiv.org/abs/1909.08565
},
year = {2019},
date = {2019-09-18},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Schütt, KT; Gastegger, M; Tkatchenko, A; Müller, KR Quantum-Chemical Insights from Interpretable Atomistic Neural Networks Book Chapter Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, pp. 311-330, Springer International Publishing, 2019, ISBN: 978-3-030-28953-9. Links | BibTeX @inbook{Schütt2019,
title = {Quantum-Chemical Insights from Interpretable Atomistic Neural Networks},
author = {KT Schütt and M Gastegger and A Tkatchenko and KR Müller},
url = {https://link.springer.com/chapter/10.1007/978-3-030-28954-6_17},
isbn = {978-3-030-28953-9},
year = {2019},
date = {2019-09-10},
booktitle = {Explainable AI: Interpreting, Explaining and Visualizing Deep Learning},
pages = {311-330},
publisher = {Springer International Publishing},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
|
Samek, W; Müller, KR Towards Explainable Artificial Intelligence Book Chapter Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, pp. 5-22, Springer International Publishing, 2019, ISBN: 978-3-030-28954-6. Links | BibTeX @inbook{Samek2019,
title = {Towards Explainable Artificial Intelligence},
author = {W Samek and KR Müller},
url = {https://link.springer.com/chapter/10.1007/978-3-030-28954-6_1},
isbn = {978-3-030-28954-6},
year = {2019},
date = {2019-09-10},
booktitle = {Explainable AI: Interpreting, Explaining and Visualizing Deep Learning},
pages = {5-22},
publisher = {Springer International Publishing},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
|
Laubichler, MD; Maienschein, J; Renn, J Computational History of Knowledge: Challenges and Opportunities Journal Article Isis, 110 (3), pp. 502–512, 2019. Links | BibTeX @article{Laubichler2019,
title = {Computational History of Knowledge: Challenges and Opportunities},
author = {MD Laubichler and J Maienschein and J Renn},
url = {https://doi.org/10.1086/705544},
year = {2019},
date = {2019-09-01},
journal = {Isis},
volume = {110},
number = {3},
pages = {502–512},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Iravani, S; Conrad, TOF Deep Learning for Proteomics Data for Feature Selection and Classification Inproceedings Machine Learning and Knowledge Extraction, pp. 301-316, Springer International Publishing, 2019, ISBN: 978-3-030-29726-8. Links | BibTeX @inproceedings{Iravani2019,
title = {Deep Learning for Proteomics Data for Feature Selection and Classification},
author = {S Iravani and TOF Conrad},
url = {https://link.springer.com/chapter/10.1007/978-3-030-29726-8_19},
isbn = {978-3-030-29726-8},
year = {2019},
date = {2019-08-23},
booktitle = {Machine Learning and Knowledge Extraction},
pages = {301-316},
publisher = {Springer International Publishing},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Hägele, M; Seegerer, P; Lapuschkin, S; Bockmayr, M; Samek, W; Klauschen, F; Müller, KR; Binder, A Resolving challenges in deep learning-basedanalyses of histopathological images usingexplanation methods Journal Article https://arxiv.org/abs/1908.06943, 2019. BibTeX @article{haeg2019,
title = {Resolving challenges in deep learning-basedanalyses of histopathological images usingexplanation methods},
author = {M Hägele and P Seegerer and S Lapuschkin and M Bockmayr and W Samek and F Klauschen and KR Müller and A Binder},
year = {2019},
date = {2019-08-15},
journal = {https://arxiv.org/abs/1908.06943},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Alber, M; Lapuschkin, S; Seegerer, P; Hägele, M; Schütt, KT; Montavon, G; Samek, W; Müller, KR; Dähne, S; Kindermans, PJ iNNvestigate neural networks! Journal Article 2019. Links | BibTeX @article{Alber2019,
title = {iNNvestigate neural networks!},
author = {M Alber and S Lapuschkin and P Seegerer and M Hägele and KT Schütt and G Montavon and W Samek and KR Müller and S Dähne and PJ Kindermans
},
url = {https://arxiv.org/abs/1808.04260
},
year = {2019},
date = {2019-08-13},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Bosse, S; Becker, S; Müller, KR; Samek, W; Wiegand, T Estimation of distortion sensitivity for visual quality prediction using a convolutional neural network Journal Article Digital Signal Processing, 91 , pp. 54-65, 2019. Links | BibTeX @article{Bosse2019,
title = {Estimation of distortion sensitivity for visual quality prediction using a convolutional neural network},
author = {S Bosse and S Becker and KR Müller and W Samek and T Wiegand
},
url = {https://www.sciencedirect.com/science/article/pii/S1051200418308868
},
year = {2019},
date = {2019-08-01},
journal = {Digital Signal Processing},
volume = {91},
pages = {54-65},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Chmiela, S; Sauceda, HE; Poltavsky, I; Müller, KR; Tkatchenko, A sGDML: Constructing accurate and data efficient molecular force fields using machine learning Journal Article Computer Physics Communications, 240 , pp. 38-45, 2019, ISSN: 0010-4655. Links | BibTeX @article{Chmiela2019,
title = {sGDML: Constructing accurate and data efficient molecular force fields using machine learning},
author = {S Chmiela and HE Sauceda and I Poltavsky and KR Müller and A Tkatchenko},
url = {https://www.sciencedirect.com/science/article/pii/S0010465519300591},
issn = {0010-4655},
year = {2019},
date = {2019-07-01},
journal = {Computer Physics Communications},
volume = {240},
pages = {38-45},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Wagner, P; Morath, JP; Zychlinsky, A; Müller, KR; Samek, W Rotation Invariant Clustering of 3D Cell Nuclei Shapes* Proceeding 2019, (41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)). Links | BibTeX @proceedings{Wagner2019,
title = {Rotation Invariant Clustering of 3D Cell Nuclei Shapes*},
author = {P Wagner and JP Morath and A Zychlinsky and KR Müller and W Samek
},
url = {http://iphome.hhi.de/samek/pdf/WagEMBC19.pdf
},
year = {2019},
date = {2019-07-01},
note = {41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)},
keywords = {},
pubstate = {published},
tppubtype = {proceedings}
}
|
Schütt, KT; Gastegger, M; Tkatchenko, A; Müller, KR; Maurer, RJ Unifying machine learning and quantum chemistry -- a deep neural network for molecular wavefunctions Journal Article 2019. Links | BibTeX @article{Schütt2019b,
title = {Unifying machine learning and quantum chemistry -- a deep neural network for molecular wavefunctions},
author = {KT Schütt and M Gastegger and A Tkatchenko and KR Müller and RJ Maurer},
url = {https://arxiv.org/abs/1906.10033},
year = {2019},
date = {2019-06-24},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Kauffmann, J; Esders, M; Montavon, G; Samek, W; Müller, KR From Clustering to Cluster Explanations via Neural Networks Journal Article 2019. Links | BibTeX @article{Kauffmann2019,
title = {From Clustering to Cluster Explanations via Neural Networks},
author = {J Kauffmann and M Esders and G Montavon and W Samek and KR Müller
},
url = {https://arxiv.org/abs/1906.07633
},
year = {2019},
date = {2019-06-18},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Ruff, L; Vandermeulen, RA; Görnitz, N; Binder, A; Müller, E; Müller, KR; Kloft, M Deep Semi-Supervised Anomaly Detection Journal Article 2019. Links | BibTeX @article{Ruff2019,
title = {Deep Semi-Supervised Anomaly Detection},
author = {L Ruff and RA Vandermeulen and N Görnitz and A Binder and E Müller and KR Müller and M Kloft},
url = {https://arxiv.org/abs/1906.02694},
year = {2019},
date = {2019-06-06},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Gebauer, NWA; Gastegger, M; Schütt, KT Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules Journal Article 2019. Links | BibTeX @article{Gebauer2019,
title = {Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules},
author = {NWA Gebauer and M Gastegger and KT Schütt},
url = {https://arxiv.org/abs/1906.00957},
year = {2019},
date = {2019-06-02},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Wiedemann, S; Müller, KR; Samek, W Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints Journal Article IEEE Transactions on Neural Networks and Learning Systems, 2019. Links | BibTeX @article{wiede2019,
title = {Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints},
author = {S Wiedemann and KR Müller and W Samek},
url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8725933&isnumber=6104215},
year = {2019},
date = {2019-05-29},
journal = { IEEE Transactions on Neural Networks and Learning Systems},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Wiedemann, S; Müller, KR; Samek, W Compact and Computationally Efficient Representation of Deep Neural Networks Journal Article IEEE Transactions on Neural Networks and Learning Systems, pp. 1-14, 2019. Links | BibTeX @article{Wiedemann2019,
title = {Compact and Computationally Efficient Representation of Deep Neural Networks},
author = {S Wiedemann and KR Müller and W Samek},
url = {https://ieeexplore.ieee.org/abstract/document/8725933},
year = {2019},
date = {2019-05-29},
journal = {IEEE Transactions on Neural Networks and Learning Systems},
pages = {1-14},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Bogojeski, M; Vogt-Maranto, L; Tuckerman, ME; Müller, KR; Burke, K Density Functionals with Quantum Chemical Accuracy: From Machine Learning to Molecular Dynamics Journal Article Forthcoming Forthcoming. Links | BibTeX @article{Bogojeski2019,
title = {Density Functionals with Quantum Chemical Accuracy: From Machine Learning to Molecular Dynamics},
author = {M Bogojeski and L Vogt-Maranto and ME Tuckerman and KR Müller and K Burke
},
url = {https://chemrxiv.org/articles/Density_Functionals_with_Quantum_Chemical_Accuracy_From_Machine_Learning_to_Molecular_Dynamics/8079917
},
year = {2019},
date = {2019-05-04},
keywords = {},
pubstate = {forthcoming},
tppubtype = {article}
}
|
Zhou, JT; Tsang, IW; KR, SS Ho; Müller, N-ary decomposition for multi-class classification Journal Article Machine Learning, 108 (5), pp. 809-830, 2019. Links | BibTeX @article{Zhou2019,
title = {N-ary decomposition for multi-class classification},
author = {JT Zhou and IW Tsang and SS Ho KR and Müller},
url = {https://link.springer.com/article/10.1007/s10994-019-05786-2},
year = {2019},
date = {2019-05-01},
journal = {Machine Learning},
volume = {108},
number = {5},
pages = {809-830},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Arras, L; Osman, A; Müller, KR; Samek, W Evaluating Recurrent Neural Network Explanations Journal Article 2019. Links | BibTeX @article{Arras2019,
title = {Evaluating Recurrent Neural Network Explanations},
author = {L Arras and A Osman and KR Müller and W Samek
},
url = {https://arxiv.org/abs/1904.11829},
year = {2019},
date = {2019-04-26},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Bauer, A; Nakajima, S; Goernitz, N; Müller, KR Partial Optimality of Dual Decomposition for MAP Inference in Pairwise MRFs Inproceedings Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS) 2019
, pp. 1696–1703, 2019. Links | BibTeX @inproceedings{Bauer2019,
title = {Partial Optimality of Dual Decomposition for MAP Inference in Pairwise MRFs},
author = {A Bauer and S Nakajima and N Goernitz and KR Müller
},
url = {http://proceedings.mlr.press/v89/bauer19b.html
},
year = {2019},
date = {2019-04-18},
booktitle = {Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS) 2019
},
volume = {89},
pages = {1696--1703},
series = {Proceedings of Machine Learning Research},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Schwenk, G; Pabst, R; Müller, KR Classification of structured validation data using stateless and stateful features Journal Article Computer Communications, 138 , pp. 54-66, 2019. Links | BibTeX @article{Schwenk2019,
title = {Classification of structured validation data using stateless and stateful features},
author = {G Schwenk and R Pabst and KR Müller},
url = {https://www.sciencedirect.com/science/article/pii/S0140366418307345},
year = {2019},
date = {2019-04-15},
journal = {Computer Communications},
volume = {138},
pages = {54-66},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Srinivasan, V; Kuruoglu, EE; Müller, KR; Samek, W; Nakajima, S Black-Box Decision based Adversarial Attack with Symmetric α-stable Distribution Journal Article 2019. Links | BibTeX @article{Srinivasan2019b,
title = {Black-Box Decision based Adversarial Attack with Symmetric α-stable Distribution},
author = {V Srinivasan and EE Kuruoglu and KR Müller and W Samek and S Nakajima},
url = {https://arxiv.org/abs/1904.05586},
year = {2019},
date = {2019-04-11},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Redyuk, S Automated Documentation of End-to-End Experiments in Data Science Inproceedings 2019 IEEE 35th International Conference on Data Engineering (ICDE), pp. 2076-2080, 2019. Links | BibTeX @inproceedings{Redyuk2019,
title = {Automated Documentation of End-to-End Experiments in Data Science},
author = {S Redyuk},
url = {https://ieeexplore.ieee.org/abstract/document/8731587},
year = {2019},
date = {2019-04-01},
booktitle = {2019 IEEE 35th International Conference on Data Engineering (ICDE)},
pages = {2076-2080},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Nicoli, K; Kessel, P; Strodthoff, N; Samek, W; Müller, KR; Nakajima, S Comment on "Solving Statistical Mechanics Using VANs": Introducing saVANt - VANs Enhanced by Importance and MCMC Sampling Journal Article 2019. Links | BibTeX @article{Nicoli2019,
title = {Comment on "Solving Statistical Mechanics Using VANs": Introducing saVANt - VANs Enhanced by Importance and MCMC Sampling},
author = {K Nicoli and P Kessel and N Strodthoff and W Samek and KR Müller and S Nakajima
},
url = {https://arxiv.org/abs/1903.11048
},
year = {2019},
date = {2019-03-26},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Lapuschkin, S; Wäldchen, S; Binder, A; Montavon, G; Müller, Samek & KR W Unmasking Clever Hans predictors and assessing what machines really learn Journal Article Nature Communications, 10 (1096), 2019. BibTeX @article{Lapuschkin2019,
title = {Unmasking Clever Hans predictors and assessing what machines really learn},
author = {S Lapuschkin and S Wäldchen and A Binder and G Montavon and W Samek & KR Müller },
year = {2019},
date = {2019-03-11},
journal = {Nature Communications},
volume = {10},
number = {1096},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Sattler, F; Wiedemann, S; Müller, KR; Samek, W Robust and Communication-Efficient Federated Learning from Non-IID Data Journal Article 2019. Links | BibTeX @article{Sattler2019,
title = {Robust and Communication-Efficient Federated Learning from Non-IID Data},
author = {F Sattler and S Wiedemann and KR Müller and W Samek},
url = {https://arxiv.org/abs/1903.02891
},
year = {2019},
date = {2019-03-07},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Helmers, L; Horn, F; Biegler, F; Oppermann, T; Müller, KR Automating the search for a patent’s prior art with a full text similarity search Journal Article PLoS ONE, 14 (3), pp. 1-17, 2019. Links | BibTeX @article{Helmers2019,
title = {Automating the search for a patent’s prior art with a full text similarity search},
author = {L Helmers and F Horn and F Biegler and T Oppermann and KR Müller
},
url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0212103
},
year = {2019},
date = {2019-03-04},
journal = {PLoS ONE},
volume = {14},
number = {3},
pages = {1-17},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Horst, F; Lapuschkin, S; Samek, W; Müller, KR; Schöllhorn, WI Explaining the unique nature of individual gait patterns with deep learning Journal Article Scientific Reports, 9 (1), pp. 2391, 2019. Links | BibTeX @article{Horst2019,
title = {Explaining the unique nature of individual gait patterns with deep learning},
author = {F Horst and S Lapuschkin and W Samek and KR Müller and WI Schöllhorn
},
url = {https://www.nature.com/articles/s41598-019-38748-8
},
year = {2019},
date = {2019-02-20},
journal = {Scientific Reports},
volume = {9},
number = {1},
pages = {2391},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Müller, KR Explainable Deep Learning for Analysing Brain Data Inproceedings 2019 7th International Winter Conference on Brain-Computer Interface (BCI)
, 2019. Links | BibTeX @inproceedings{Mueller2019,
title = {Explainable Deep Learning for Analysing Brain Data},
author = {KR Müller
},
url = {https://ieeexplore.ieee.org/document/8737321
},
year = {2019},
date = {2019-02-01},
booktitle = {2019 7th International Winter Conference on Brain-Computer Interface (BCI)
},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Montavon, G Gradient-Based Vs. Propagation-Based Explanations: An Axiomatic Comparison Book Chapter Explainable AI: Interpreting, Explaining and Visualizing Deep Learning
, pp. 253-265, Springer International Publishing, 2019, ISBN: 978-3-030-28954-6. Links | BibTeX @inbook{Montavon2019,
title = {Gradient-Based Vs. Propagation-Based Explanations: An Axiomatic Comparison},
author = {G Montavon
},
url = {https://link.springer.com/chapter/10.1007/978-3-030-28954-6_13
},
isbn = {978-3-030-28954-6},
year = {2019},
date = {2019-01-01},
booktitle = {Explainable AI: Interpreting, Explaining and Visualizing Deep Learning
},
pages = {253-265},
publisher = {Springer International Publishing},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
|
Montavon, G; Binder, A; Lapuschkin, S; Samek, W; Müller, KR Layer-Wise Relevance Propagation: An Overview Book Chapter Explainable AI: Interpreting, Explaining and Visualizing Deep Learning
, pp. 193-209, Springer International Publishing, 2019, ISBN: 978-3-030-28954-6. Links | BibTeX @inbook{Montavon2019b,
title = {Layer-Wise Relevance Propagation: An Overview},
author = {G Montavon and A Binder and S Lapuschkin and W Samek and KR Müller
},
url = {https://link.springer.com/chapter/10.1007/978-3-030-28954-6_10
},
isbn = {978-3-030-28954-6},
year = {2019},
date = {2019-01-01},
booktitle = {Explainable AI: Interpreting, Explaining and Visualizing Deep Learning
},
pages = {193-209},
publisher = {Springer International Publishing},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
|
Jurmeister, P; Bockmayr, M; Seegerer, P; Bockmayr, T; Treue, D; Montavon, G; Vollbrecht, C; Arnold, A; Teichmann, D; Bressem, K; Schüller, U; von Laffert, M; Müller, KR; Capper, D; Klauschen, F Machine learning analysis of DNA methylation profiles distinguishes primary lung squamous cell carcinomas from head and neck metastases Journal Article Science Translational Medicine, 2019. Links | BibTeX @article{Jurmeister2019,
title = {Machine learning analysis of DNA methylation profiles distinguishes primary lung squamous cell carcinomas from head and neck metastases},
author = {P Jurmeister and M Bockmayr and P Seegerer and T Bockmayr and D Treue and G Montavon and C Vollbrecht and A Arnold and D Teichmann and K Bressem and U Schüller and M von Laffert and KR Müller and D Capper and F Klauschen
},
url = {https://stm.sciencemag.org/content/11/509/eaaw8513
},
year = {2019},
date = {2019-01-01},
journal = {Science Translational Medicine},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Sauceda, HE; Chmiela, S; Poltavsky, I; Müller, KR; Tkatchenko, A Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forces Journal Article The Journal of Chemical Physics, 150 (11), 2019. Links | BibTeX @article{Sauceda2019,
title = {Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forces},
author = {HE Sauceda and S Chmiela and I Poltavsky and KR Müller and A Tkatchenko
},
url = {https://aip.scitation.org/doi/10.1063/1.5078687
},
year = {2019},
date = {2019-01-01},
journal = {The Journal of Chemical Physics},
volume = {150},
number = {11},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Anders, CJ; Montavon, G; Samek, W; Müller, KR Understanding Patch-Based Learning of Video Data by Explaining Predictions Book Chapter Explainable AI: Interpreting, Explaining and Visualizing Deep Learning
, pp. 297-309, Springer International Publishing, 2019, ISBN: 978-3-030-28954-6. Links | BibTeX @inbook{Anders2019,
title = {Understanding Patch-Based Learning of Video Data by Explaining Predictions},
author = {CJ Anders and G Montavon and W Samek and KR Müller
},
url = {https://link.springer.com/chapter/10.1007/978-3-030-28954-6_16
},
isbn = {978-3-030-28954-6},
year = {2019},
date = {2019-01-01},
booktitle = {Explainable AI: Interpreting, Explaining and Visualizing Deep Learning
},
pages = {297-309},
publisher = {Springer International Publishing},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
|
Srinivasan, V; Marban, A; Müller, KR; Samek, W; Nakajima, S Defense Against Adversarial Attacks by Langevin Dynamics Proceeding 2019, (ICML 2019 Workshop on Uncertainty and Robustness in Deep Learning). Links | BibTeX @proceedings{Srinivasan2019,
title = {Defense Against Adversarial Attacks by Langevin Dynamics},
author = {V Srinivasan and A Marban and KR Müller and W Samek and S Nakajima},
url = {http://iphome.hhi.de/samek/pdf/SriICML19.pdf},
year = {2019},
date = {2019-01-01},
note = {ICML 2019 Workshop on Uncertainty and Robustness in Deep Learning},
keywords = {},
pubstate = {published},
tppubtype = {proceedings}
}
|
2018
|
Wiedemann, S; Marban, A; Müller, KR; Samek, W Entropy-Constrained Training of Deep Neural Networks Journal Article 2018. Links | BibTeX @article{Wiedemann2018,
title = {Entropy-Constrained Training of Deep Neural Networks},
author = {S Wiedemann and A Marban and KR Müller and W Samek},
url = {https://arxiv.org/abs/1812.07520
},
year = {2018},
date = {2018-12-19},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Breβ, S; Köcher, B; Funke, H; Zeuch, S; Rabl, T; Markl, V Generating custom code for efficient query execution on heterogeneous processors Journal Article The International Journal on Very Large Data Bases, 27 (6), pp. 797-822, 2018. BibTeX @article{Breβ2018,
title = {Generating custom code for efficient query execution on heterogeneous processors},
author = {S Breβ and B Köcher and H Funke and S Zeuch and T Rabl and V Markl},
year = {2018},
date = {2018-12-01},
journal = {The International Journal on Very Large Data Bases},
volume = {27},
number = {6},
pages = {797-822},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Klus, S; Bittracher, A; Schuster, I; Schütte, C A kernel-based approach to molecular conformation analysis Journal Article Journal of Chemical Physics, 149 (24), 2018. BibTeX @article{Klus2018,
title = {A kernel-based approach to molecular conformation analysis},
author = {S Klus and A Bittracher and I Schuster and C Schütte},
year = {2018},
date = {2018-12-01},
journal = {Journal of Chemical Physics},
volume = {149},
number = {24},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Gebauer, NWA; Gastegger, M; Schütt, KT Generating equilibrium molecules with deep neural networks Journal Article 2018. Links | BibTeX @article{Gebauer2018,
title = {Generating equilibrium molecules with deep neural networks},
author = {NWA Gebauer and M Gastegger and KT Schütt
},
url = {https://arxiv.org/abs/1810.11347
},
year = {2018},
date = {2018-10-26},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Nicoli, KA; and M and KT Schütt, Kessel Gastegger P Analysis of Atomistic Representations Using Weighted Skip-Connections Proceeding 2018, (NIPS 2018 Workshop: Machine Learning for Molecules and Materials). Links | BibTeX @proceedings{Nicoli2018,
title = {Analysis of Atomistic Representations Using Weighted Skip-Connections},
author = {KA Nicoli and P Kessel and M Gastegger and KT Schütt
},
url = {https://arxiv.org/abs/1810.09751
},
year = {2018},
date = {2018-10-23},
note = {NIPS 2018 Workshop: Machine Learning for Molecules and Materials},
keywords = {},
pubstate = {published},
tppubtype = {proceedings}
}
|
Kaltenstadler, S; Nakajima, Shinichi; and W Samek, KR Müller Wasserstein Stationary Subspace Analysis Journal Article IEEE Journal of Selected Topics in Signal Processing, 12 (6), pp. 1213-1223, 2018. Links | BibTeX @article{Kaltenstadler2018,
title = {Wasserstein Stationary Subspace Analysis},
author = {S Kaltenstadler and Shinichi Nakajima and KR Müller and W Samek},
url = {https://ieeexplore.ieee.org/abstract/document/8481426},
year = {2018},
date = {2018-10-04},
journal = {IEEE Journal of Selected Topics in Signal Processing},
volume = {12},
number = {6},
pages = {1213-1223},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Meyer, A; Zverinski, D; Pfahringer, B; Kempfert, J; Kuehne, T; Sündermann, S; Stamm, C; Hofmann, T; Falk, V; Eickhoff, C Machine learning for real-time prediction of complications in critical care: a retrospective study Journal Article The Lancet Respiratory Medicine, 6 , 2018. BibTeX @article{Meyer2018,
title = {Machine learning for real-time prediction of complications in critical care: a retrospective study},
author = {A Meyer and D Zverinski and B Pfahringer and J Kempfert and T Kuehne and S Sündermann and C Stamm and T Hofmann and V Falk and C Eickhoff},
year = {2018},
date = {2018-09-01},
journal = {The Lancet Respiratory Medicine},
volume = {6},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Sattler, F; Wiedemann, S; Müller, KR; Samek, W Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communication Journal Article 2018. Links | BibTeX @article{Sattler2018,
title = {Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communication},
author = {F Sattler and S Wiedemann and KR Müller and W Samek},
url = {https://arxiv.org/abs/1805.08768
},
year = {2018},
date = {2018-05-22},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Reisenhofer, R; Bosse, S; Kutyniok, G; Wiegand, T A Haar wavelet-based perceptual similarity index for image quality assessment Journal Article Signal Processing: Image Communication, 61 , pp. 31-33, 2018. BibTeX @article{Reisenhofer2018,
title = {A Haar wavelet-based perceptual similarity index for image quality assessment},
author = {R Reisenhofer and S Bosse and G Kutyniok and T Wiegand},
year = {2018},
date = {2018-02-01},
journal = {Signal Processing: Image Communication},
volume = {61},
pages = {31-33},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
