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  • Currently displaying 1 - 20 of 214 publications
Equilibria of an anisotropic nonlocal interaction equation: Analysis and numerics
JA Carrillo, B During, LM Kreusser, CB Schonlieb
– Discrete and Continuous Dynamical Systems
(2021)
41,
3985
Error Analysis for Probabilities of Rare Events with Approximate Models
F Wagner, J Latz, I Papaioannou, E Ullmann
– SIAM Journal on Numerical Analysis
(2021)
59,
1948
Equivariant neural networks for inverse problems
E Celledoni, MJ Ehrhardt, C Etmann, B Owren, C-B Schönlieb, F Sherry
– Inverse Problems
(2021)
On the extremal points of the ball of the Benamou–Brenier energy
K Bredies, M Carioni, S Fanzon, F Romero
– Bulletin of the London Mathematical Society
(2021)
blms.12509
Weighted Sparse Subspace Representation: A Unified Framework for Subspace Clustering, Constrained Clustering, and Active Learning
H Peng, NG Pavlidis
– arXiv
(2021)
HERS Superpixels: Deep Affinity Learning for Hierarchical Entropy Rate Segmentation.
H Peng, AI Aviles-Rivero, C-B Schonlieb
– arXiv
(2021)
abs/2106.03755,
Structure-preserving deep learning
E Celledoni, MJ Ehrhardt, C Etmann, RI McLachlan, B Owren, CB Schonlieb, F Sherry
– European Journal of Applied Mathematics
(2021)
1
Machine churning
M Roberts
– NEW SCIENTIST
(2021)
245,
23
Analysis of stochastic gradient descent in continuous time
J Latz
– Statistics and Computing
(2021)
31,
39
Mechanisms Underlying Vascular Endothelial Growth Factor Receptor Inhibition-Induced Hypertension: The HYPAZ Trial.
KM Mäki-Petäjä, A McGeoch, LL Yang, A Hubsch, CM McEniery, PAR Meyer, F Mir, P Gajendragadkar, N Ramenatte, G Anandappa, S Santos Franco, SJ Bond, C-B Schönlieb, Y Boink, C Brune, IB Wilkinson, DI Jodrell, J Cheriyan
– Hypertension (Dallas, Tex. : 1979)
(2021)
77,
1591
Adversarially Learned Iterative Reconstruction for Imaging Inverse Problems
S Mukherjee, O Öktem, CB Schönlieb
– Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
(2021)
12679,
540
A deep-learning pipeline for the diagnosis and discrimination of viral, non-viral and COVID-19 pneumonia from chest X-ray images.
G Wang, X Liu, J Shen, C Wang, Z Li, L Ye, X Wu, T Chen, K Wang, X Zhang, Z Zhou, J Yang, Y Sang, R Deng, W Liang, T Yu, M Gao, J Wang, Z Yang, H Cai, G Lu, L Zhang, L Yang, W Xu, W Wang, A Olevera, I Ziyar, C Zhang, O Li, W Liao, J Liu, W Chen, W Chen, J Shi, L Zheng, L Zhang, Z Yan, X Zou, G Lin, G Cao, LL Lau, L Mo, Y Liang, M Roberts, E Sala, C-B Schönlieb, M Fok, JY-N Lau, T Xu, J He, K Zhang, W Li, T Lin
– Nature Biomedical Engineering
(2021)
5,
509
Unsupervised Image Restoration Using Partially Linear Denoisers.
R Ke, C-B Schonlieb
– IEEE transactions on pattern analysis and machine intelligence
(2021)
PP,
1
Dynamic spectral residual superpixels
J Zhang, AI Aviles-Rivero, D Heydecker, X Zhuang, R Chan, CB Schönlieb
– Pattern Recognition
(2021)
112,
107705
Classification and image processing with a semi-discrete scheme for fidelity forced Allen–Cahn on graphs
J Budd, Y van Gennip, J Latz
– GAMM-Mitteilungen
(2021)
44,
Machine learning for COVID-19 diagnosis and prognostication: lessons for amplifying the signal whilst reducing the noise
D Driggs, I Selby, M Roberts, E Gkrania-Klotsas, JHF Rudd, G Yang, J Babar, E Sala, C-B Schönlieb, AIX-COVNET collaboration
– Radiology: Artificial Intelligence
(2021)
3,
e210011
Duality-based Higher-order Non-smooth Optimization on Manifolds
W Diepeveen, J Lellmann
– arXiv
(2021)
Rethinking medical image reconstruction via shape prior, going deeper and faster: Deep joint indirect registration and reconstruction
J Liu, AI Aviles-Rivero, H Ji, C-B Schönlieb
– Medical Image Analysis
(2021)
68,
101930
Compressed Sensing Plus Motion (CS+M): A New Perspective for Improving Undersampled MR Image Reconstruction.
AI Aviles-Rivero, N Debroux, G Williams, MJ Graves, C-B Schönlieb
– Medical image analysis
(2021)
68,
101933
Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
M Roberts, D Driggs, M Thorpe, J Gilbey, M Yeung, S Ursprung, AI Aviles-Rivero, C Etmann, C McCague, L Beer, JR Weir-McCall, Z Teng, E Gkrania-Klotsas, A Ruggiero, A Korhonen, E Jefferson, E Ako, G Langs, G Gozaliasl, G Yang, H Prosch, J Preller, J Stanczuk, J Tang, J Hofmanninger, J Babar, LE Sánchez, M Thillai, PM Gonzalez, P Teare, X Zhu, M Patel, C Cafolla, H Azadbakht, J Jacob, J Lowe, K Zhang, K Bradley, M Wassin, M Holzer, K Ji, MD Ortet, T Ai, N Walton, P Lio, S Stranks, T Shadbahr, W Lin, Y Zha, Z Niu, JHF Rudd, E Sala, CB Schönlieb
– Nature Machine Intelligence
(2021)
3,
199