no code implementations • 19 Dec 2023 • Shutong Jin, Ruiyu Wang, Florian T. Pokorny
Even though large-scale text-to-image generative models show promising performance in synthesizing high-quality images, applying these models directly to image editing remains a significant challenge.
no code implementations • 3 Oct 2023 • Shutong Jin, Ruiyu Wang, Muhammad Zahid, Florian T. Pokorny
As model and dataset sizes continue to scale in robot learning, the need to understand what is the specific factor in the dataset that affects model performance becomes increasingly urgent to ensure cost-effective data collection and model performance.
no code implementations • 16 Jun 2022 • Alexander Kravberg, Giovanni Luca Marchetti, Vladislav Polianskii, Anastasiia Varava, Florian T. Pokorny, Danica Kragic
We introduce an algorithm for active function approximation based on nearest neighbor regression.
no code implementations • 25 Jul 2016 • Majd Hawasly, Florian T. Pokorny, Subramanian Ramamoorthy
Autonomous robots operating in dynamic environments must maintain beliefs over a hypothesis space that is rich enough to represent the activities of interest at different scales.
no code implementations • 26 Jan 2015 • Andrea Baisero, Florian T. Pokorny, Carl Henrik Ek
In many applications data is naturally presented in terms of orderings of some basic elements or symbols.
no code implementations • NeurIPS 2012 • Florian T. Pokorny, Hedvig Kjellström, Danica Kragic, Carl Ek
We present a novel method for learning densities with bounded support which enables us to incorporate `hard' topological constraints.