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Books and Theses
- 2023
[b1]Imant Daunhawer:
Multimodal Representation Learning under Weak Supervision. ETH Zurich, Zürich, Switzerland, 2023
Conference and Workshop Papers
- 2024
- [c10]Mike Laszkiewicz
, Imant Daunhawer, Julia E. Vogt, Asja Fischer, Johannes Lederer:
Benchmarking the Fairness of Image Upsampling Methods. FAccT 2024: 489-517 - 2023
- [c9]Imant Daunhawer, Alice Bizeul, Emanuele Palumbo, Alexander Marx, Julia E. Vogt:
Identifiability Results for Multimodal Contrastive Learning. ICLR 2023 - [c8]Emanuele Palumbo, Imant Daunhawer, Julia E. Vogt:
MMVAE+: Enhancing the Generative Quality of Multimodal VAEs without Compromises. ICLR 2023 - [c7]Yuge Shi, Imant Daunhawer, Julia E. Vogt, Philip H. S. Torr, Amartya Sanyal:
How robust is unsupervised representation learning to distribution shift? ICLR 2023 - 2022
- [c6]Imant Daunhawer, Thomas M. Sutter, Kieran Chin-Cheong, Emanuele Palumbo, Julia E. Vogt:
On the Limitations of Multimodal VAEs. ICLR 2022 - 2021
- [c5]Juan M. Montoya, Imant Daunhawer, Julia E. Vogt, Marco A. Wiering:
Decoupling State Representation Methods from Reinforcement Learning in Car Racing. ICAART (2) 2021: 752-759 - [c4]Thomas M. Sutter, Imant Daunhawer, Julia E. Vogt:
Generalized Multimodal ELBO. ICLR 2021 - 2020
- [c3]Varaha Karthik Pattisapu, Imant Daunhawer, Thomas J. Weikert, Alexander Sauter, Bram Stieltjes, Julia E. Vogt:
PET-Guided Attention Network for Segmentation of Lung Tumors from PET/CT Images. GCPR 2020: 445-458 - [c2]Imant Daunhawer, Thomas M. Sutter, Ricards Marcinkevics, Julia E. Vogt:
Self-supervised Disentanglement of Modality-Specific and Shared Factors Improves Multimodal Generative Models. GCPR 2020: 459-473 - [c1]Thomas M. Sutter, Imant Daunhawer, Julia E. Vogt:
Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence. NeurIPS 2020
Informal and Other Publications
- 2024
- [i7]Mike Laszkiewicz, Imant Daunhawer, Julia E. Vogt, Asja Fischer, Johannes Lederer:
Benchmarking the Fairness of Image Upsampling Methods. CoRR abs/2401.13555 (2024) - 2023
- [i6]Imant Daunhawer, Alice Bizeul, Emanuele Palumbo, Alexander Marx, Julia E. Vogt:
Identifiability Results for Multimodal Contrastive Learning. CoRR abs/2303.09166 (2023) - 2022
- [i5]Yuge Shi, Imant Daunhawer, Julia E. Vogt, Philip H. S. Torr, Amartya Sanyal:
How robust are pre-trained models to distribution shift? CoRR abs/2206.08871 (2022) - 2021
- [i4]Thomas M. Sutter, Imant Daunhawer, Julia E. Vogt:
Generalized Multimodal ELBO. CoRR abs/2105.02470 (2021) - [i3]Imant Daunhawer, Thomas M. Sutter, Kieran Chin-Cheong, Emanuele Palumbo, Julia E. Vogt:
On the Limitations of Multimodal VAEs. CoRR abs/2110.04121 (2021) - 2020
- [i2]Thomas M. Sutter, Imant Daunhawer, Julia E. Vogt:
Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence. CoRR abs/2006.08242 (2020) - 2019
- [i1]Thomas M. Sutter, Imant Daunhawer, Julia E. Vogt:
Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence. ViGIL@NeurIPS 2019
Coauthor Index
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