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of 1 097
pro vyhledávání: '"P. A. Binz"'
In this paper, a distributed dual-quaternion multiplicative extended Kalman filter for the estimation of poses and velocities of individual satellites in a fleet of spacecraft is analyzed. The proposed algorithm uses both absolute and relative pose m
Externí odkaz:
http://arxiv.org/abs/2411.19033
In-context learning, the ability to adapt based on a few examples in the input prompt, is a ubiquitous feature of large language models (LLMs). However, as LLMs' in-context learning abilities continue to improve, understanding this phenomenon mechani
Externí odkaz:
http://arxiv.org/abs/2410.01280
Autor:
Veeraragavan, Narasimha Raghavan, Tabatabaei, Mohammad Hossein, Elvatun, Severin, Vallevik, Vibeke Binz, Larønningen, Siri, Nygård, Jan F
Synthetic data generation is increasingly recognized as a crucial solution to address data related challenges such as scarcity, bias, and privacy concerns. As synthetic data proliferates, the need for a robust evaluation framework to select a synthet
Externí odkaz:
http://arxiv.org/abs/2405.07196
Autor:
Samak, Tanmay Vilas, Samak, Chinmay Vilas, Binz, Joey, Smereka, Jonathon, Brudnak, Mark, Gorsich, David, Luo, Feng, Krovi, Venkat
Publikováno v:
SAE Technical Paper 2024-01-4111
Off-road autonomy validation presents unique challenges due to the unpredictable and dynamic nature of off-road environments. Traditional methods focusing on sequentially sweeping across the parameter space for variability analysis struggle to compre
Externí odkaz:
http://arxiv.org/abs/2405.04743
Large language models (LLMs) have significantly advanced the field of artificial intelligence. Yet, evaluating them comprehensively remains challenging. We argue that this is partly due to the predominant focus on performance metrics in most benchmar
Externí odkaz:
http://arxiv.org/abs/2402.18225
We study the in-context learning dynamics of large language models (LLMs) using three instrumental learning tasks adapted from cognitive psychology. We find that LLMs update their beliefs in an asymmetric manner and learn more from better-than-expect
Externí odkaz:
http://arxiv.org/abs/2402.03969
Publikováno v:
Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria. PMLR 235, 2024
Ecological rationality refers to the notion that humans are rational agents adapted to their environment. However, testing this theory remains challenging due to two reasons: the difficulty in defining what tasks are ecologically valid and building r
Externí odkaz:
http://arxiv.org/abs/2402.01821
Autor:
Vallevik, Vibeke Binz, Babic, Aleksandar, Marshall, Serena Elizabeth, Elvatun, Severin, Brøgger, Helga, Alagaratnam, Sharmini, Edwin, Bjørn, Veeraragavan, Narasimha Raghavan, Befring, Anne Kjersti, Nygård, Jan Franz
Publikováno v:
Int. J. Med. Inform.185 (2024)
Ensuring safe adoption of AI tools in healthcare hinges on access to sufficient data for training, testing and validation. In response to privacy concerns and regulatory requirements, using synthetic data has been suggested. Synthetic data is created
Externí odkaz:
http://arxiv.org/abs/2401.13716
As children grow older, they develop an intuitive understanding of the physical processes around them. Their physical understanding develops in stages, moving along developmental trajectories which have been mapped out extensively in previous empiric
Externí odkaz:
http://arxiv.org/abs/2310.19943
Evaluating alignment between humans and neural network representations in image-based learning tasks
Autor:
Demircan, Can, Saanum, Tankred, Pettini, Leonardo, Binz, Marcel, Baczkowski, Blazej M, Doeller, Christian F, Garvert, Mona M, Schulz, Eric
Humans represent scenes and objects in rich feature spaces, carrying information that allows us to generalise about category memberships and abstract functions with few examples. What determines whether a neural network model generalises like a human
Externí odkaz:
http://arxiv.org/abs/2306.09377