Laura Hannemose Rieger

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Lars Kai HansenProfessor, Cognitive Systems, DTU Compute, Technical University of DenmarkVerified email at dtu.dk
Tejs VeggeProfessor, Technical University of DenmarkVerified email at dtu.dk
Eibar FloresSINTEF IndustryVerified email at sintef.no
Chandan SinghSenior researcher, Microsoft researchVerified email at microsoft.com
Arghya BhowmikTenured Associate Professor, Technical University of Denmark, Energy conversion and storageVerified email at stanford.edu
W. James MurdochFounder, Breakpoint AIVerified email at berkeley.edu
Bin YUProfessor of Statistics and EECS, UC BerkeleyVerified email at stat.berkeley.edu
Ole WintherBiology, Univ of Copenhagen, Genomic Medicine, Rigshospitalet and Technical University of DenmarkVerified email at bio.ku.dk
Ivano E. CastelliProfessor, Autonomous Materials Discovery, DTU EnergyVerified email at dtu.dk
Grégoire MontavonGuest professor, Freie Universität BerlinVerified email at fu-berlin.de
Pattarawat ChormaiTechnische Universität Berlin and Max Planck School of CognitionVerified email at tu-berlin.de
Klaus-Robert MüllerGoogle DeepMind & TU Berlin & Korea University & Max Planck Institute for Informatics, GermanyVerified email at tu-berlin.de
Max WilsonPostdoc Technical University of DenmarkVerified email at dtu.dk
Helge S. SteinTechnical University of MunichVerified email at tum.de
Hamidreza HajiyaniResearcher in Dassault SystèmesVerified email at uni-due.de
Felix HankeCuspAIVerified email at cusp.ai
Giovanni PizziLaboratory for Materials Simulations, Paul Scherrer Institute (PSI), Villigen PSI, SwitzerlandVerified email at psi.ch
Jonas BuskTechnical University of DenmarkVerified email at dtu.dk
Simon ClarkSINTEFVerified email at sintef.no
Simon K. SteensenPhD fellow, Technical University of DenmarkVerified email at dtu.dk

Laura Hannemose Rieger

Other namesLaura Rieger, Laura Simone Rieger

Technical University of Denmark - DTU

Verified email at dtu.dk - Homepage

material science machine learning deep learning materials design


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Interpretations are useful: penalizing explanations to align neural networks with prior knowledge L Rieger, C Singh, W Murdoch, B Yu International conference on machine learning, 8116-8126, 2020	254	2020
A simple defense against adversarial attacks on heatmap explanations L Rieger, LK Hansen 2020 Workshop on Human Interpretability in Machine Learning (WHI), 2020	62*	2020
IROF: a low resource evaluation metric for explanation methods L Rieger, LK Hansen Workshop AI for Affordable Healthcare at ICLR 2020, Addis Ababa, Ethiopia, 2020	52	2020
Interpretability in intelligent systems–a new concept? LK Hansen, L Rieger Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, 41-49, 2019	51	2019
Uncertainty-aware and explainable machine learning for early prediction of battery degradation trajectory LH Rieger, E Flores, KF Nielsen, P Norby, E Ayerbe, O Winther, T Vegge, ... Digital Discovery 2 (1), 112-122, 2023	19	2023
Structuring neural networks for more explainable predictions L Rieger, P Chormai, G Montavon, LK Hansen, KR Müller Explainable and Interpretable Models in Computer Vision and Machine Learning …, 2018	15	2018
Understanding the patterns that neural networks learn from chemical spectra LH Rieger, M Wilson, T Vegge, E Flores Digital Discovery 2 (6), 1957-1968, 2023	5	2023
Client Adaptation improves Federated Learning with Simulated Non-IID Clients L Rieger, RMT Høegh, LK Hansen International Workshop on Federated Learning for User Privacy and Data …, 2020	4	2020
Separable explanations of neural network decisions L Rieger NIPS 2017 Workshop on Interpretable Machine Learning, 2017	4	2017
Autonomous Battery Optimization by Deploying Distributed Experiments and Simulations M Vogler, SK Steensen, FF Ramírez, L Merker, J Busk, JM Carlsson, ... Advanced Energy Materials 14 (46), 2403263, 2024	3*	2024
Unravelling degradation mechanisms and overpotential sources in aged and non-aged batteries: A non-invasive diagnosis WA Appiah, LH Rieger, E Flores, T Vegge, A Bhowmik Journal of Energy Storage 84, 111000, 2024	2	2024
Tunnel effect in CNNS: image reconstruction from max switch locations MLR Saint Andre, L Rieger, M Hannemose, J Kim IEEE Signal Processing Letters 24 (3), 254-258, 2016	2	2016
PerQueue: managing complex and dynamic workflows BH Sjølin, WS Hansen, AA Morin-Martinez, MH Petersen, LH Rieger, ... Digital Discovery 3 (9), 1832-1841, 2024	1	2024
Setting the standard for machine learning in phase field prediction: a benchmark dataset and baseline metrics LH Rieger, K Zelič, I Mele, T Katrašnik, A Bhowmik Scientific data 11 (1), 1275, 2024		2024
Utilizing active learning to accelerate segmentation of microstructures with tiny annotation budgets LH Rieger, F Cadiou, Q Jacquet, V Vanpeene, J Villanova, S Lyonnard, ... Energy Storage Materials 73, 103785, 2024		2024
Explainability for neural networks L Rieger Technical University of Denmark, 2020		2020

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