• Matematiktorvet, 303 B

    DK-2800 Kgs. Lyngby

    Denmark

Research Output 2011 2020

Filter
Book chapter
2019

Explainable ai – preface

Samek, W., Montavon, G., Vedaldi, A., Hansen, L. K. & Müller, K. R., 1 Jan 2019, Explainable AI: Interpreting, Explaining and Visualizing Deep Learning. Springer, p. v-vii (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 11700).

Research output: Chapter in Book/Report/Conference proceedingBook chapterResearchpeer-review

Fundamental Structures in Temporal Communication Networks

Lehmann, S., 2019, Temporal Network Theory. Computational Social Sciences. Holme, P. & Saramäki, J. (eds.). Springer, p. 25-48 24 p. (Computational Social Sciences ).

Research output: Chapter in Book/Report/Conference proceedingBook chapterResearchpeer-review

Interpretability in Intelligent Systems – A New Concept?

Hansen, L. K. & Rieger, L., 1 Jan 2019, Artificial Intelligence and Lecture Notes in Bioinformatics). Springer, p. 41-49 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 11700 LNCS).

Research output: Chapter in Book/Report/Conference proceedingBook chapterResearchpeer-review

22 Downloads (Pure)

SimNIBS 2.1: A Comprehensive Pipeline for Individualized Electric Field Modelling for Transcranial Brain Stimulation

Saturnino, G. B., Puonti, O., Nielsen, J. D., Antonenko, D., Madsen, K. H. & Thielscher, A., 2019, Brain and Human Body Modeling. Springer, p. 3-25

Research output: Chapter in Book/Report/Conference proceedingBook chapterResearchpeer-review

Open Access
File

Structuring Neural Networks for More Explainable Predictions

Rieger, L., Chormai, P., Montavon, G., Hansen, L. K. & Müller, K-R., 2019, Explainable and Interpretable Models in Computer Vision and Machine Learning. Springer, p. 115-131

Research output: Chapter in Book/Report/Conference proceedingBook chapterResearchpeer-review

The 1-Person Laboratory of the Quantified Self Community

Christiansen, T. B., Kristensen, D. B. & Larsen, J. E., 2019, Metric Culture: Ontologies of Self-Tracking Practices. Emerald Group Publishing, p. 97-115

Research output: Chapter in Book/Report/Conference proceedingBook chapterResearchpeer-review

2017

Modeling the Temporal Nature of Human Behavior for Demographics Prediction

Felbo, B., Sundsøy, P., Pentland, A., Jørgensen, S. L. & Montjoye, Y-A., 2017, Machine Learning and Knowledge Discovery in Databases. Altun, Y., Das, K., Mielikäinen, T., Malerba, D., Stefanowski, J., Read, J., Žitnik, M., Ceci, M. & Džeroski, S. (eds.). Springer, Vol. 10536. p. 140-152 13 p. (Lecture Notes in Computer Science).

Research output: Chapter in Book/Report/Conference proceedingBook chapterResearchpeer-review

2014

Analyzing Social Interactions: Promises and Challenges of Cross Recurrence Quantification Analysis

Fusaroli, R., Konvalinka, I. & Wallot, S., 2014, Translational Recurrences: From Mathematical Theory to Real-World Applications. Marwan, N., Riley, M., Giuliani, A. & Webber Jr., C. L. (eds.). Springer, p. 137-155 (Springer Proceedings in Mathematics & Statistics, Vol. 103).

Research output: Chapter in Book/Report/Conference proceedingBook chapterResearchpeer-review