Imant Daunhawer

22 papers A* 8Journal 8Unranked 5

Year	Rank	Type	Title / Venue / Authors
2025	A*	`conf`	From Logits to Hierarchies: Hierarchical Clustering made Simple. ICML Emanuele Palumbo, Moritz Vandenhirtz, Alain Ryser, Imant Daunhawer, Julia E. Vogt
2025	A*	`conf`	scSSL-Bench: Benchmarking Self-Supervised Learning for Single-Cell Data. ICML Olga Ovcharenko, Florian Barkmann, Philip Toma, Imant Daunhawer, Julia E. Vogt, Sebastian Schelter, Valentina Boeva
2025	J	`jnl`	scSSL-Bench: Benchmarking Self-Supervised Learning for Single-Cell Data. CoRR Olga Ovcharenko, Florian Barkmann, Philip Toma, Imant Daunhawer, Julia E. Vogt, Sebastian Schelter, Valentina Boeva
2024	—	`conf`	Benchmarking the Fairness of Image Upsampling Methods. FAccT Mike Laszkiewicz, Imant Daunhawer, Julia E. Vogt, Asja Fischer, Johannes Lederer
2024	J	`jnl`	Benchmarking the Fairness of Image Upsampling Methods. CoRR Mike Laszkiewicz, Imant Daunhawer, Julia E. Vogt, Asja Fischer, Johannes Lederer
2024	J	`jnl`	From Logits to Hierarchies: Hierarchical Clustering made Simple. CoRR Emanuele Palumbo, Moritz Vandenhirtz, Alain Ryser, Imant Daunhawer, Julia E. Vogt
2023	A*	`conf`	How robust is unsupervised representation learning to distribution shift? ICLR Yuge Shi, Imant Daunhawer, Julia E. Vogt, Philip H. S. Torr, Amartya Sanyal
2023	A*	`conf`	Identifiability Results for Multimodal Contrastive Learning. ICLR Imant Daunhawer, Alice Bizeul, Emanuele Palumbo, Alexander Marx, Julia E. Vogt
2023	J	`jnl`	Identifiability Results for Multimodal Contrastive Learning. CoRR Imant Daunhawer, Alice Bizeul, Emanuele Palumbo, Alexander Marx, Julia E. Vogt
2023	A*	`conf`	MMVAE+: Enhancing the Generative Quality of Multimodal VAEs without Compromises. ICLR Emanuele Palumbo, Imant Daunhawer, Julia E. Vogt
2023	—	`—`	Multimodal Representation Learning under Weak Supervision. Imant Daunhawer
2022	J	`jnl`	How robust are pre-trained models to distribution shift? CoRR Yuge Shi, Imant Daunhawer, Julia E. Vogt, Philip H. S. Torr, Amartya Sanyal
2022	A*	`conf`	On the Limitations of Multimodal VAEs. ICLR Imant Daunhawer, Thomas M. Sutter, Kieran Chin-Cheong, Emanuele Palumbo, Julia E. Vogt
2021	—	`conf`	Decoupling State Representation Methods from Reinforcement Learning in Car Racing. ICAART (2) Juan M. Montoya, Imant Daunhawer, Julia E. Vogt, Marco A. Wiering
2021	A*	`conf`	Generalized Multimodal ELBO. ICLR Thomas M. Sutter, Imant Daunhawer, Julia E. Vogt
2021	J	`jnl`	Generalized Multimodal ELBO. CoRR Thomas M. Sutter, Imant Daunhawer, Julia E. Vogt
2021	J	`jnl`	On the Limitations of Multimodal VAEs. CoRR Imant Daunhawer, Thomas M. Sutter, Kieran Chin-Cheong, Emanuele Palumbo, Julia E. Vogt
2020	A*	`conf`	Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence. NeurIPS Thomas M. Sutter, Imant Daunhawer, Julia E. Vogt
2020	J	`jnl`	Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence. CoRR Thomas M. Sutter, Imant Daunhawer, Julia E. Vogt
2020	—	`conf`	PET-Guided Attention Network for Segmentation of Lung Tumors from PET/CT Images. GCPR Varaha Karthik Pattisapu, Imant Daunhawer, Thomas J. Weikert, Alexander Sauter, Bram Stieltjes, Julia E. Vogt
2020	—	`conf`	Self-supervised Disentanglement of Modality-Specific and Shared Factors Improves Multimodal Generative Models. GCPR Imant Daunhawer, Thomas M. Sutter, Ricards Marcinkevics, Julia E. Vogt
2019	—	`conf`	Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence. ViGIL@NeurIPS Thomas M. Sutter, Imant Daunhawer, Julia E. Vogt