Personal information

Activities

Collapse all

Employment (1)

Danmarks Tekniske Universitet: Lyngby, DK

2020-11-11 to present | Postdoc (DTU Energy)
Employment
Source: Self-asserted source
Laura Hannemose Rieger

Education and qualifications (3)

KAIST: Daejeon, KR

2015-08 to present | Computer Science Msc (Computer Science)
Education
Source: Self-asserted source
Laura Hannemose Rieger

Technical University of Denmark: Kongens Lyngby, Hovedstaden, DK

2017-09-01 to 2020-10-31 | Phd (DTU Compute)
Education
Source: Self-asserted source
Laura Hannemose Rieger

TU Berlin: Berlin, DE

2015-08 to 2017-07 | Msc Computer Science (Elektrotechnik und Informatik)
Education
Source: Self-asserted source
Laura Hannemose Rieger

Works (17)

Correction: Understanding the patterns that neural networks learn from chemical spectra

Digital Discovery
2024 | Journal article
DOI: 10.1039/D4DD90005G
Contributors: Laura Hannemose Rieger; Max Wilson; Tejs Vegge; Eibar Flores
Source: check_circle
Crossref

PerQueue: managing complex and dynamic workflows

Digital Discovery
2024 | Journal article
DOI: 10.1039/D4DD00134F
Contributors: Benjamin Heckscher Sjølin; William Sandholt Hansen; Armando Antonio Morin-Martinez; Martin Hoffmann Petersen; Laura Hannemose Rieger; Tejs Vegge; Juan Maria García-Lastra; Ivano E. Castelli
Source: check_circle
Crossref

Autonomous Battery Optimization by Deploying Distributed Experiments and Simulations

Advanced Energy Materials
2024-12 | Journal article
DOI: 10.1002/aenm.202403263
Contributors: Monika Vogler; Simon Krarup Steensen; Francisco Fernando Ramírez; Leon Merker; Jonas Busk; Johan Martin Carlsson; Laura Hannemose Rieger; Bojing Zhang; François Liot; Giovanni Pizzi et al.
Source: check_circle
Crossref

Utilizing active learning to accelerate segmentation of microstructures with tiny annotation budgets

Energy Storage Materials
2024-11 | Journal article
DOI: 10.1016/j.ensm.2024.103785
Contributors: Laura Hannemose Rieger; François Cadiou; Quentin Jacquet; Victor Vanpeene; Julie Villanova; Sandrine Lyonnard; Tejs Vegge; Arghya Bhowmik
Source: check_circle
Crossref

Setting the standard for machine learning in phase field prediction: a benchmark dataset and baseline metrics

Scientific Data
2024-11-23 | Journal article
DOI: 10.1038/s41597-024-04128-9
Contributors: Laura Hannemose Rieger; Klemen Zelič; Igor Mele; Tomaž Katrašnik; Arghya Bhowmik
Source: check_circle
Crossref

Autonomous battery optimisation by deploying distributed experiments and simulations

2024-05-16 | Preprint
DOI: 10.26434/chemrxiv-2024-vfq1n
Contributors: Monika Vogler; Simon Steensen; Francisco Ramirez; Leon Merker; Jonas Busk; Johan M. Carlsson; Laura Rieger; Bojing Zhang; Francois Liot; Giovanni Pizzi et al.
Source: check_circle
Crossref

Unravelling degradation mechanisms and overpotential sources in aged and non-aged batteries: A non-invasive diagnosis

Journal of Energy Storage
2024-04 | Journal article
DOI: 10.1016/j.est.2024.111000
Part of ISSN: 2352-152X
Contributors: Williams Agyei Appiah; Laura Hannemose Rieger; Eibar Flores; Tejs Vegge; Arghya Bhowmik
Source: Self-asserted source
Laura Hannemose Rieger

Uncertainty-aware and explainable machine learning for early prediction of battery degradation trajectory

Digital Discovery
2023 | Journal article
DOI: 10.1039/D2DD00067A
Contributors: Laura Hannemose Rieger; Eibar Flores; Kristian Frellesen Nielsen; Poul Norby; Elixabete Ayerbe; Ole Winther; Tejs Vegge; Arghya Bhowmik
Source: check_circle
Crossref
grade
Preferred source (of 3)‎

Understanding the patterns that neural networks learn from chemical spectra

Digital Discovery
2023 | Journal article | Author
DOI: 10.1039/d3dd00203a
Part of ISSN: 2635-098X
Contributors: Laura Hannemose Rieger; Max Wilson; Tejs Vegge; Eibar Flores
Source: Self-asserted source
Laura Hannemose Rieger
grade
Preferred source (of 4)‎

A simple defense against adversarial attacks on heatmap explanations

5th Annual Workshop on Human Interpretability in Machine Learning
2020 | Conference paper
ARXIV: arXiv:1903.00519v1
Contributors: Rieger, Laura; Hansen, Lars Kai
Source: Self-asserted source
Laura Hannemose Rieger
grade
Preferred source (of 2)‎

Client Adaptation improves Federated Learning with Simulated Non-IID Clients

International Workshop on Federated Learning for User Privacy and Data Confidentiality in Conjunction with ICML 2020
2020 | Conference paper
Contributors: Rieger, Laura; Høegh, Rasmus Malik Thaarup; Hansen, Lars Kai
Source: Self-asserted source
Laura Hannemose Rieger

Interpretations are Useful: Penalizing Explanations to Align Neural Networks with Prior Knowledge

Proceedings of the 37th International Conference on Machine Learning
2020 | Conference paper
ARXIV: arXiv:1909.13584v2
URI:

https://proceedings.mlr.press/v119/rieger20a.html
Contributors: Rieger, Laura; Singh, Chandan; Murdoch, William; Yu, Bin; III, Hal Daumé; Singh, Aarti
Source: Self-asserted source
Laura Hannemose Rieger
grade
Preferred source (of 2)‎

IROF: a low resource evaluation metric for explanation methods

Workshop AI for Affordable Healthcare at ICLR 2020
2020 | Conference paper
Contributors: Rieger, Laura; Hansen, Lars Kai
Source: Self-asserted source
Laura Hannemose Rieger

Interpretability in Intelligent Systems A New Concept?

Explainable AI: Interpreting, Explaining and Visualizing Deep Learning
2019 | Book chapter
DOI: 10.1007/978-3-030-28954-6_3
Source: Self-asserted source
Laura Hannemose Rieger

Structuring Neural Networks for More Explainable Predictions

Explainable and Interpretable Models in Computer Vision and Machine Learning
2018 | Book chapter
DOI: 10.1007/978-3-319-98131-4_5
Source: Self-asserted source
Laura Hannemose Rieger

Separable explanations of neural network decisions

Proceedings Workshop on Interpreting, Explaining and Visualizing Deep Learning (at NIPS)
2017 | Journal article
URI:

https://orbit.dtu.dk/en/publications/id(1a8c2782-79cc-418b-97ff-ae97cec0b6e9).html
Source: Self-asserted source
Laura Hannemose Rieger

Tunnel Effect in CNNs: Image Reconstruction From Max Switch Locations

IEEE Signal Processing Letters
2017-03 | Journal article
DOI: 10.1109/lsp.2016.2638435
Part of ISSN: 1070-9908
Part of ISSN: 1558-2361
Source: Self-asserted source
Laura Hannemose Rieger
How many people are using ORCID?