Mario Wieser
Cited by
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Determinants of HIV-1 reservoir size and long-term dynamics during suppressive ART
N Bachmann, C von Siebenthal, V Vongrad, T Turk, K Neumann, ...
Nature communications 10 (1), 1-11, 2019
Information bottleneck for estimating treatment effects with systematically missing covariates
S Parbhoo, M Wieser, A Wieczorek, V Roth
Entropy 22 (4), 389, 2020
Learning sparse latent representations with the deep copula information bottleneck
A Wieczorek*, M Wieser*, D Murezzan, V Roth
International Conference on Learning Representations (ICLR), 2018
Deep Archetypal Analysis
SM Keller, M Samarin, M Wieser, V Roth
German Conference on Pattern Recognition, 2019
Greedy Structure Learning of Hierarchical Compositional Models
A Kortylewski, A Wieczorek, M Wieser, C Blumer, S Parbhoo, ...
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019
Learning extremal representations with deep archetypal analysis
SM Keller, M Samarin, FA Torres, M Wieser, V Roth
International Journal of Computer Vision (IJCV) 129 (4), 805-820, 2021
Informed MCMC with Bayesian Neural Networks for Facial Image Analysis
A Kortylewski, M Wieser, A Morel-Forster, A Wieczorek, S Parbhoo, ...
NeurIPS Bayesian Deep Learning Workshop, 2018
3DMolNet: a generative network for molecular structures
V Nesterov, M Wieser, V Roth
arXiv preprint arXiv:2010.06477, 2020
Transfer Learning from Well-Curated to Less-Resourced Populations with HIV
S Parbhoo, M Wieser, V Roth, F Doshi-Velez
Machine Learning for Healthcare (MLHC), 2020
Inverse Learning of Symmetries
M Wieser, S Parbhoo, A Wieczorek, V Roth
Advances in Neural Information Processing Systems 33, 2020
Host genomics of the HIV-1 reservoir size and its decay rate during suppressive antiretroviral treatment
CW Thorball, A Borghesi, N Bachmann, C Von Siebenthal, V Vongrad, ...
JAIDS Journal of Acquired Immune Deficiency Syndromes 85 (4), 517-524, 2020
Learning Invariant Representations for Deep Latent Variable Models
M Wieser
University_of_Basel, 2020
Self-Supervised Representation Learning for High-Content Screening
D Siegismund*, M Wieser*, S Heyse, S Steigele
Learning Conditional Invariance Through Cycle Consistency
M Samarin, V Nesterov, M Wieser, A Wieczorek, S Parbhoo, V Roth
DAGM German Conference on Pattern Recognition, 376-391, 2021
Estimating Causal Effects With Partial Covariates For Clinical Interpretability
S Parbhoo, M Wieser, V Roth
NeurIPS Machine Learning for Health Workshop (ML4H), 2018
A modular prototyping hard-and software platform for faster development of intelligent charging infrastructures of electric vehicles
J Clement, M Wieser, P Benoit, R Kohrs, C Wittwer
4th International Conference on Power Engineering, Energy and Electrical†…, 2013
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