Finding the input features that reduce the entropy of a neural network’s prediction

This file was created by the TYPO3 extension bib --- Timezone: CEST Creation date: 2024-04-20 Creation time: 08-54-42 --- Number of references 18 article AmanovaME2024 Finding the input features that reduce the entropy of a neural network’s prediction Applied Intelligence 2024 1 25 8.4,8.42,ML 10.1007/s10489-024-05277-5 NAmanova JMartin CElster article FallerAvME2023 About the Generalizability of Deep Learning based Image Quality Assessment in Mammography Machine Learning: Science and Technology 2023 9 12 8.4,8.42,ML accepted 10.1088/2632-2153/acf914 JFaller NAmanova R Evan Engen JMartin CElster article MarschallWSE2023 Machine learning based priors for Bayesian inversion in MR imaging Metrologia 2023 7 4 60 4 8.4,8.42,LargeScaleDataAna,ML 10.1088/1681-7575/ace3c2 MMarschall GWübbeler FSchmähling CElster article BrahmaKMSK2023 Data-efficient Bayesian learning for radial dynamic MR reconstruction Medical Physics 2023 6 27 8.4,8.42,ML,Messunsicherheit,LargeScaleDataAna 10.1002/mp.16543 SBrahma CKolbitsch JMartin TSchäffter AKofler article MarschallWSE2022 Generative models and Bayesian inversion using Laplace approximation Computational Statistics 2023 3 16 8.4,8.42,ML,LargeScaleDataAna 10.1007/s00180-023-01345-5 MMarschall GWübbeler FSchmähling CElster article MartinE2021_2 Errors-in-Variables for deep learning: rethinking aleatoric uncertainty Neural Processing Letters 2022 11 1 8.4,8.42,ML 1573-773X 10.1007/s11063-022-11066-3 JMartin CElster article OlbrichRKLvBOS2022 Deep learning based liquid level extraction from video observations of gas-liquid flows International Journal of Multiphase Flow 2022 9 10 8.4,8.41,Flow,ML https://doi.org/10.1016/j.ijmultiphaseflow.2022.104247 M.Olbrich L.Riazy T.Kretz T.Leonard D.S.van Putten M.Bär K.Oberleithner S.Schmelter phdthesis HarrenneeHoffmann2022 Investigating deep ensembles for the tilted-wave interferometer 2022 8 15 publiziert 8.4,8.42,ML,Form https://depositonce.tu-berlin.de/bitstream/11303/17264/4/harren_lara.pdf TU Berlin PhD Thesis 10.14279/depositonce-16044 LHarren née Hoffmann article SchmahlingME2021 A framework for benchmarking uncertainty in deep regression Applied Intelligence 2022 8 9 8.4,8.42,ML 10.1007/s10489-022-03908-3 FrankoSchmähling JörgMartin ClemensElster article AmanovaME2022 Explainability for deep learning in mammography image quality assessment Machine Learning: Science and Technology 2022 6 17 8.4,8.42,ML accepted 10.1088/2632-2153/ac7a03 NAmanova JMartin CElster article MehariS2021 Self-supervised representation learning from 12-lead ECG data Computers in Biology and Medicine 2021 12 18 141 105114 8.4,8.41,ML https://doi.org/10.1016/j.compbiomed.2021.105114 TMehari NStrodthoff article HoffmannFE2021_2 Deep learning for tilted-wave interferometry tm - Technisches Messen 2021 11 20 publiziert 8.4,8.42,Form,ML 10.1515/teme-2021-0103 LHoffmann IFortmeier CElster article HoffmannFE2021 Uncertainty Quantification by Ensemble Learning for Computational Optical Form Measurements Machine Learning: Science and Technology 2021 5 24 publiziert 8.4,8.42,ML,Form 10.1088/2632-2153/ac0495 LHoffmann IFortmeier CElster article MartinE2020_3 Detecting unusual input to neural networks Appl Intell 2020 10 30 publiziert 8.4,8.42,ML 10.1007/s10489-020-01925-8 JMartin CElster phdthesis Kretz2020 Development of model observers for quantitative assessment of mammography image quality 2020 10 7 publiziert 8.4,8.42,ML http://dx.doi.org/10.14279/depositonce-10552 TU Berlin PhD Thesis TKretz article HoffmannE2020 Deep Neural Networks for Computational Optical Form Measurements Journal of Sensors and Sensor Systems 2020 9 24 9 301--307 publiziert 8.4,8.42,ML,Form 10.5194/jsss-9-301-2020 LHoffmann CElster article KretzMSE2020 Mammography Image Quality Assurance Using Deep Learning IEEE Transactions on Biomedical Engineering 2020 4 14 publiziert 8.4,8.42,ML 10.1109/TBME.2020.2983539 TKretz K-RMüller TSchäffter CElster article MartinE2020 Inspecting adversarial examples using the fisher information Neurocomputing 2020 3 21 382 80--86 publiziert 8.4,8.42,ML 10.1016/j.neucom.2019.11.052 JMartin CElster