2 papers
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…
cs.DS2020
Dynamic Matching Algorithms in Practice
Monika Henzinger, Shahbaz Khan, Richard Paul +1
In recent years, significant advances have been made in the design and analysis of fully dynamic maximal matching algorithms. However, these theoretical results have received very…