Zobrazeno 1 - 10
of 17 357
pro vyhledávání: '"A Renz"'
Autor:
Igelbrink, Felix, Renz, Marian, Günther, Martin, Powell, Piper, Niecksch, Lennart, Lima, Oscar, Atzmueller, Martin, Hertzberg, Joachim
Semantic mapping is a key component of robots operating in and interacting with objects in structured environments. Traditionally, geometric and knowledge representations within a semantic map have only been loosely integrated. However, recent advanc
Externí odkaz:
http://arxiv.org/abs/2411.18147
Autor:
Renz, Jessica, Dauda, Kazeem A., Aga, Olav N. L., Diaz-Uriarte, Ramon, Löhr, Iren H., Blomberg, Bjørn, Johnston, Iain G.
Can we understand and predict the evolutionary pathways by which bacteria acquire multi-drug resistance (MDR)? These questions have substantial potential impact in basic biology and in applied approaches to address the global health challenge of anti
Externí odkaz:
http://arxiv.org/abs/2411.00219
Autor:
Beth, Christian, Fleischmann, Pamela, Huch, Annika, Kazempour, Daniyal, Kröger, Peer, Kulow, Andrea, Renz, Matthias
In 2017 Day et al. introduced the notion of locality as a structural complexity-measure for patterns in the field of pattern matching established by Angluin in 1980. In 2019 Casel et al. showed that determining the locality of an arbitrary pattern is
Externí odkaz:
http://arxiv.org/abs/2410.00601
Discovering Motifs to Fingerprint Multi-Layer Networks: a Case Study on the Connectome of C. Elegans
Motif discovery is a powerful and insightful method to quantify network structures and explore their function. As a case study, we present a comprehensive analysis of regulatory motifs in the connectome of the model organism Caenorhabditis elegans (C
Externí odkaz:
http://arxiv.org/abs/2408.13263
Autor:
Renz, Katrin, Chen, Long, Marcu, Ana-Maria, Hünermann, Jan, Hanotte, Benoit, Karnsund, Alice, Shotton, Jamie, Arani, Elahe, Sinavski, Oleg
In this technical report, we present CarLLaVA, a Vision Language Model (VLM) for autonomous driving, developed for the CARLA Autonomous Driving Challenge 2.0. CarLLaVA uses the vision encoder of the LLaVA VLM and the LLaMA architecture as backbone, a
Externí odkaz:
http://arxiv.org/abs/2406.10165
Real-world autonomous driving systems must make safe decisions in the face of rare and diverse traffic scenarios. Current state-of-the-art planners are mostly evaluated on real-world datasets like nuScenes (open-loop) or nuPlan (closed-loop). In part
Externí odkaz:
http://arxiv.org/abs/2404.07569
Autor:
Schwarting, Julian, Holzberger, Fabian, Muhr, Markus, Renz, Martin, Boeckh-Behrens, Tobias, Wohlmuth, Barbara, Kirschke, Jan
Rupture of intracranial aneurysms results in severe subarachnoidal hemorrhage, which is associated with high morbidity and mortality. Neurointerventional occlusion of the aneurysm through coiling has evolved to a therapeutical standard. The choice of
Externí odkaz:
http://arxiv.org/abs/2403.06889
Autor:
Taveekitworachai, Pittawat, Abdullah, Febri, Dewantoro, Mury F., Xia, Yi, Suntichaikul, Pratch, Thawonmas, Ruck, Togelius, Julian, Renz, Jochen
This paper presents the second ChatGPT4PCG competition at the 2024 IEEE Conference on Games. In this edition of the competition, we follow the first edition, but make several improvements and changes. We introduce a new evaluation metric along with a
Externí odkaz:
http://arxiv.org/abs/2403.02610
Autor:
Sima, Chonghao, Renz, Katrin, Chitta, Kashyap, Chen, Li, Zhang, Hanxue, Xie, Chengen, Beißwenger, Jens, Luo, Ping, Geiger, Andreas, Li, Hongyang
We study how vision-language models (VLMs) trained on web-scale data can be integrated into end-to-end driving systems to boost generalization and enable interactivity with human users. While recent approaches adapt VLMs to driving via single-round v
Externí odkaz:
http://arxiv.org/abs/2312.14150
Novelty adaptation is the ability of an intelligent agent to adjust its behavior in response to changes in its environment. This is an important characteristic of intelligent agents, as it allows them to continue to function effectively in novel or u
Externí odkaz:
http://arxiv.org/abs/2312.11138