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Imant Daunhawer
Imant Daunhawer
Verified email at ethz.ch
Title
Cited by
Cited by
Year
Generalized Multimodal ELBO
TM Sutter*, I Daunhawer*, JE Vogt
International Conference on Learning Representations, 2021
682021
Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence
TM Sutter, I Daunhawer, JE Vogt
Advances in Neural Information Processing Systems, 2020
612020
Pharmacometrics and machine learning partner to advance clinical data analysis
G Koch, M Pfister, I Daunhawer, M Wilbaux, S Wellmann, JE Vogt
Clinical Pharmacology & Therapeutics 107 (4), 926-933, 2020
532020
Enhanced early prediction of clinically relevant neonatal hyperbilirubinemia with machine learning
I Daunhawer*, S Kasser*, G Koch, L Sieber, H Cakal, J Tütsch, M Pfister, ...
Pediatric research 86 (1), 122-127, 2019
492019
How robust is unsupervised representation learning to distribution shift?
Y Shi, I Daunhawer, JE Vogt, PHS Torr, A Sanyal
International Conference on Learning Representations, 2022
25*2022
On the Limitations of Multimodal VAEs
I Daunhawer, TM Sutter, K Chin-Cheong, E Palumbo, JE Vogt
International Conference on Learning Representations, 2021
252021
Self-supervised disentanglement of modality-specific and shared factors improves multimodal generative models
I Daunhawer, TM Sutter, R Marcinkevičs, JE Vogt
German Conference on Pattern Recognition, 459-473, 2020
242020
Identifiability Results for Multimodal Contrastive Learning
I Daunhawer, A Bizeul, E Palumbo, A Marx, JE Vogt
International Conference on Learning Representations, 2023
162023
MMVAE+: Enhancing the generative quality of multimodal VAEs without compromises
E Palumbo, I Daunhawer, JE Vogt
International Conference on Learning Representations, 2023
132023
Machine learning used to compare the diagnostic accuracy of risk factors, clinical signs and biomarkers and to develop a new prediction model for neonatal early-onset sepsis
M Stocker*, I Daunhawer*, W Van Herk, S El Helou, S Dutta, ...
The Pediatric Infectious Disease Journal 41 (3), 248-254, 2021
92021
Biases in the football betting market
I Daunhawer, D Schoch, S Kosub
Social Science Research Network, 2017
42017
Decoupling State Representation Methods from Reinforcement Learning in Car Racing
JM Montoya, I Daunhawer, JE Vogt, MA Wiering
International Conference on Agents and Artificial Intelligence, 752-759, 2021
32021
PET-guided Attention Network for Segmentation of Lung Tumors from PET/CT images
VK Pattisapu, I Daunhawer, T Weikert, A Sauter, B Stieltjes, JE Vogt
German Conference on Pattern Recognition, 445-458, 2020
32020
Benchmarking the Fairness of Image Upsampling Methods
M Laszkiewicz, I Daunhawer, JE Vogt, A Fischer, J Lederer
arXiv preprint arXiv:2401.13555, 2024
12024
Multimodal Representation Learning under Weak Supervision
I Daunhawer
ETH Zurich, 2023
2023
Validating the early phototherapy prediction tool across cohorts
I Daunhawer*, K Schumacher*, A Badura, JE Vogt, H Michel, S Wellmann
Frontiers in Pediatrics 11, 2023
2023
3DIdentBox: A Toolbox for Identifiability Benchmarking
A Bizeul, I Daunhawer, E Palumbo, B Schölkopf, A Marx, JE Vogt
Conference on Causal Learning and Reasoning, 2023
2023
Improving Multimodal Generative Models with Disentangled Latent Partitions
I Daunhawer, TM Sutter, JE Vogt
Bayesian Deep Learning workshop, NeurIPS 2019, 2019
2019
Evolutionary algorithms for the discovery of trading rules in high-frequency betting markets
I Daunhawer
University of Konstanz, 2018
2018
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Articles 1–19