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  • Currently displaying 301 - 320 of 1413 publications
A variational model dedicated to joint segmentation, registration, and atlas generation for shape analysis
N Debroux, J Aston, F Bonardi, A Forbes, C Le Guyader, M Romanchikova, C-B Schönlieb
– SIAM Journal on Imaging Sciences
(2020)
13,
351
(doi: 10.1137/19M1271907)
Dynamic network formation with foresighted agents
Y Song, M van der Schaar
– International Journal of Game Theory
(2020)
49,
345
(doi: 10.1007/s00182-020-00714-4)
A hybrid analytical-numerical algorithm for determining the neuronal current via electroencephalography
P Hashemzadeh, AS Fokas, CB Schönlieb
– Journal of The Royal Society Interface
(2020)
17,
20190831
(doi: 10.1098/rsif.2019.0831)
Compressed sensing MRI with variable density averaging (CS-VDA) outperforms full sampling at low SNR.
J Schoormans, GJ Strijkers, AC Hansen, AJ Nederveen, BF Coolen
– Physics in Medicine & Biology
(2020)
65,
045004
(doi: 10.1088/1361-6560/ab63b7)
Bregman Itoh–Abe Methods for Sparse Optimisation
M Benning, ES Riis, C-B Schönlieb
– Journal of Mathematical Imaging and Vision
(2020)
62,
842
(doi: 10.1007/s10851-020-00944-x)
Electro-magnetoencephalography for a spherical multiple-shell model: Novel integral operators with singular-value decompositions
S Leweke, V Michel, AS Fokas
– Inverse Problems
(2020)
36,
035003
(doi: 10.1088/1361-6420/ab291f)
Three-dimensional Segmentation of Trees Through a Flexible Multi-Class Graph Cut Algorithm (MCGC)
J Williams, C Schoenlieb, T Swinfield, J Lee, X Cai, L Qie, D Coomes
– IEEE Transactions on Geoscience and Remote Sensing
(2020)
58,
754
(doi: 10.1109/TGRS.2019.2940146)
3D Segmentation of Trees through a Flexible Multiclass Graph Cut Algorithm
J Williams, C-B Schönlieb, T Swinfield, J Lee, X Cai, L Qie, DA Coomes
(2020)
(doi: 10.1109/TGRS.2019.2940146)
Should We Trust Algorithms?
D Spiegelhalter
– Harvard Data Science Review
(2020)
(doi: 10.1162/99608f92.cb91a35a)
Sparse Principal Component Analysis via Axis-Aligned Random Projections
M Gataric, T Wang, RJ Samworth
– Journal of the Royal Statistical Society Series B: Statistical Methodology
(2020)
82,
329
(doi: 10.1111/rssb.12360)
Greater aortic inflammation and calcification in abdominal aortic aneurysmal disease than atherosclerosis: a prospective matched cohort study.
NV Joshi, M Elkhawad, RO Forsythe, OMB McBride, NK Rajani, JM Tarkin, MM Chowdhury, E Donoghue, JMJ Robson, JR Boyle, TD Fryer, Y Huang, Z Teng, MR Dweck, AA Tawakol, JH Gillard, PA Coughlin, IB Wilkinson, DE Newby, JHF Rudd
– Open heart
(2020)
7,
e001141
(doi: 10.1136/openhrt-2019-001141)
iUNets: Learnable Invertible Up- and Downsampling for Large-Scale Inverse Problems
C Etmann, R Ke, C-B Schönlieb
– 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP)
(2020)
00,
1
(doi: 10.1109/mlsp49062.2020.9231874)
A multi-contrast MRI approach to thalamus segmentation.
V Corona, J Lellmann, P Nestor, C-B Schönlieb, J Acosta-Cabronero
– Human brain mapping
(2020)
abs/1807.10757,
2104
(doi: 10.1002/hbm.24933)
Superpixel Contracted Graph-Based Learning for Hyperspectral Image Classification
P Sellars, AI Aviles-Rivero, C-B Schönlieb
– IEEE Transactions on Geoscience and Remote Sensing
(2020)
58,
4180
(doi: 10.1109/TGRS.2019.2961599)
The effects of communicating uncertainty on public trust in facts and numbers
AM van der Bles, SVD Linden, A Freeman, D Spiegelhalter
(2020)
(doi: 10.31234/osf.io/vbtg3)
Deeply learned spectral total variation decomposition
TG Grossmann, Y Korolev, G Gilboa, CB Schönlieb
– Advances in Neural Information Processing Systems
(2020)
2020-December,
(link to publication)
Risk perceptions of COVID-19 around the world
S Dryhurst, CR Schneider, J Kerr, ALJ Freeman, G Recchia, AM van der Bles, D Spiegelhalter, S van der Linden
– Journal of Risk Research
(2020)
23,
994
(doi: 10.1080/13669877.2020.1758193)
Linearizable boundary value problems for the elliptic sine-Gordon and the elliptic Ernst equations
J Lenells, AS Fokas
– Journal of Nonlinear Mathematical Physics
(2020)
27,
337
(doi: 10.1080/14029251.2020.1700649)
Introducing <em>The Art of Statistics: How to Learn from Data</em>
D Spiegelhalter
– Numeracy
(2020)
13,
1
(doi: 10.5038/1936-4660.13.1.7)
Tuning-free plug-and-play proximal algorithm for inverse imaging problems
K Wei, A Aviles-Rivero, J Liang, Y Fu, CB Schönlieb, H Huang
– 37th International Conference on Machine Learning Icml 2020
(2020)
PartF168147-14,
10089
(link to publication)

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