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stat.ML2024
Stein Variational Newton Neural Network Ensembles
Klemens Flöge, Mohammed Abdul Moeed, Vincent Fortuin
Deep neural network ensembles are powerful tools for uncertainty quantification, which have recently been re-interpreted from a Bayesian perspective. However, current methods inade…
cs.LG2024
OneProt: Towards Multi-Modal Protein Foundation Models
Klemens Flöge, Srisruthi Udayakumar, Johanna Sommer +8
Recent advances in Artificial Intelligence have enabled multi-modal systems to model and translate diverse information spaces. Extending beyond text and vision, we introduce OnePro…
cs.CV2024
Parameter-efficient Bayesian Neural Networks for Uncertainty-aware Depth Estimation
Richard D. Paul, Alessio Quercia, Vincent Fortuin +2
State-of-the-art computer vision tasks, like monocular depth estimation (MDE), rely heavily on large, modern Transformer-based architectures. However, their application in safety-c…