| 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 | 302 | 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 | 80 | 2020 |
| A simple defense against adversarial attacks on heatmap explanations L Rieger, LK Hansen 2020 Workshop on Human Interpretability in Machine Learning (WHI), 2020 | 78* | 2020 |
| Interpretability in intelligent systems–a new concept? LK Hansen, L Rieger Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, 41-49, 2019 | 59 | 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 | 30 | 2023 |
| 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 | 19 | 2024 |
| 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 | 16 | 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 | 16 | 2018 |
| PerQueue: managing complex and dynamic workflows BH Sjølin, WS Hansen, AA Morin-Martinez, MH Petersen, LH Rieger, ... Digital Discovery 3 (9), 1832-1841, 2024 | 10 | 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 | 6 | 2024 |
| 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 | 6 | 2020 |
| 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 | 5 | 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 | 4 | 2024 |
| Separable explanations of neural network decisions L Rieger NIPS 2017 Workshop on Interpretable Machine Learning, 2017 | 3 | 2017 |
| 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 | 3 | 2016 |
| Explainability for neural networks L Rieger Technical University of Denmark, 2020 | | 2020 |
Lars Kai HansenProfessor, Cognitive Systems, DTU Compute, Technical University of DenmarkVerified email at dtu.dk
