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--- Timezone: CET
Creation date: 2024-03-29
Creation time: 03-27-09
--- 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