Hossein Azizpour
Hossein Azizpour
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Zitiert von
Zitiert von
CNN features off-the-shelf: an astounding baseline for recognition
A Sharif Razavian, H Azizpour, J Sullivan, S Carlsson
Proceedings of the IEEE conference on computer vision and pattern …, 2014
The role of artificial intelligence in achieving the Sustainable Development Goals
R Vinuesa, H Azizpour, I Leite, M Balaam, V Dignum, S Domisch, ...
Nature communications 11 (1), 1-10, 2020
From generic to specific deep representations for visual recognition
H Azizpour, AS Razavian, J Sullivan, A Maki, S Carlsson
Computer Vision and Pattern Recognition Workshops (CVPRW), 2015 IEEE …, 2015
Factors of transferability for a generic convnet representation
H Azizpour, A Razavian, J Sullivan, A Maki, S Carlsson
IEEE Transactions on Pattern Analysis and Machine Intelligence 38 (9), 1790-1802, 2016
Digital image analysis in breast pathology—from image processing techniques to artificial intelligence
S Robertson, H Azizpour, K Smith, J Hartman
Translational Research 194, 19-35, 2018
Explainability Techniques for Graph Convolutional Networks
F Baldassarre, H Azizpour
International Conference on Machine Learning (ICML), 2019 Workshop on …, 2019
Object Detection Using Strongly-Supervised Deformable Part Models
H Azizpour, I Laptev
European Conference on Computer Vision (ECCV), 2012
Bayesian uncertainty estimation for batch normalized deep networks
M Teye, H Azizpour, K Smith
International Conference on Machine Learning (ICML), 2018, 2018
Predictions of turbulent shear flows using deep neural networks
PA Srinivasan, L Guastoni, H Azizpour, P Schlatter, R Vinuesa
Physical Review Fluids 4 (5), 054603, 2019
Convolutional-network models to predict wall-bounded turbulence from wall quantities
L Guastoni, A Güemes, A Ianiro, S Discetti, P Schlatter, H Azizpour, ...
Journal of Fluid Mechanics 928, A27, 2021
Comparison of a deep learning risk score and standard mammographic density score for breast cancer risk prediction
K Dembrower, Y Liu, H Azizpour, M Eklund, K Smith, P Lindholm, F Strand
Radiology 294 (2), 265-272, 2020
Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels
E Englesson, H Azizpour
Advances in Neural Information Processing Systems (NeurIPS), 2021
From coarse wall measurements to turbulent velocity fields with deep learning
A Güemes, H Tober, S Discetti, A Ianiro, B Sirmacek, H Azizpour, ...
Physics of fluids 33 (7), 2021
GraphQA: Protein model quality assessment using graph convolutional networks
F Baldassarre, DM Hurtado, A Elofsson, H Azizpour
Bioinformatics, 2020
Multi-view Body Part Recognition with Random Forests
V Kazemi, M Burenius, H Azizpour, J Sullivan
British Machine Vision Conference (BMVC) 2013, 2013
Phenotypic image analysis software tools for exploring and understanding big image data from cell-based assays
K Smith, F Piccinini, T Balassa, K Koos, T Danka, H Azizpour, P Horvath
Cell systems 6 (6), 636-653, 2018
Recurrent neural networks and Koopman-based frameworks for temporal predictions in a low-order model of turbulence
H Eivazi, L Guastoni, P Schlatter, H Azizpour, R Vinuesa
International Journal of Heat and Fluid Flow 90, 108816, 2021
The role of artificial intelligence in achieving the sustainable development goals. Nat Commun 11: 233
R Vinuesa, H Azizpour, I Leite, M Balaam, V Dignum, S Domisch, ...
Discovering drag reduction strategies in wall-bounded turbulent flows using deep reinforcement learning
L Guastoni, J Rabault, P Schlatter, R Vinuesa, H Azizpour
ICLR 2023 Workshop on Physics for Machine Learning, 2023
Mixture Component Identification and Learning for Visual Recognition
O Aghazadeh, H Azizpour, J Sullivan, S Carlsson
European Conference on Computer Vision (ECCV), 2012
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