Zobrazeno 1 - 10
of 119 485
pro vyhledávání: '"A. FINN"'
Autor:
Conger, Kim, Rudnick, Gregory, Finn, Rose A., Castignani, Gianluca, Moustakas, John, Vulcani, Benedetta, Zakharova, Daria, Xie, Lizhi, Combes, Francoise, Jablonka, Pascale, Bahé, Yannick, De Lucia, Gabriella, Desai, Vandana, Koopmann, Rebecca A., Norman, Dara, Townsend, Melinda, Zaritsky, Dennis
Recent theoretical work and targeted observational studies suggest that filaments are sites of galaxy preprocessing. The aim of the WISESize project is to directly probe galaxies over the full range of environments to quantify and characterize extrin
Externí odkaz:
http://arxiv.org/abs/2411.02352
Autor:
Chernyakova, M., Malyshev, D., van Soelen, B., Galagher, A. Finn, Matchett, N., Russell, T. D., Eijnden, J. van den, Lower, M. E., Johnston, S., Tsygankov, S., Salganik, A., Shebalkova, Iu.
PSR B1259-63 is a gamma-ray binary system with a 48 ms radio pulsar orbiting around an O9.5Ve star, LS 2883, in a highly eccentric ~3.4 yr long orbit. Close to the periastron the system is detected from radio up to the TeV energies due to the interac
Externí odkaz:
http://arxiv.org/abs/2411.02128
Autor:
Mirchandani, Suvir, Yuan, David D., Burns, Kaylee, Islam, Md Sazzad, Zhao, Tony Z., Finn, Chelsea, Sadigh, Dorsa
In recent years, imitation learning from large-scale human demonstrations has emerged as a promising paradigm for training robot policies. However, the burden of collecting large quantities of human demonstrations is significant in terms of collectio
Externí odkaz:
http://arxiv.org/abs/2411.01915
Autor:
Black, Kevin, Brown, Noah, Driess, Danny, Esmail, Adnan, Equi, Michael, Finn, Chelsea, Fusai, Niccolo, Groom, Lachy, Hausman, Karol, Ichter, Brian, Jakubczak, Szymon, Jones, Tim, Ke, Liyiming, Levine, Sergey, Li-Bell, Adrian, Mothukuri, Mohith, Nair, Suraj, Pertsch, Karl, Shi, Lucy Xiaoyang, Tanner, James, Vuong, Quan, Walling, Anna, Wang, Haohuan, Zhilinsky, Ury
Robot learning holds tremendous promise to unlock the full potential of flexible, general, and dexterous robot systems, as well as to address some of the deepest questions in artificial intelligence. However, bringing robot learning to the level of g
Externí odkaz:
http://arxiv.org/abs/2410.24164
The hallucinations of large language models (LLMs) are increasingly mitigated by allowing LLMs to search for information and to ground their answers in real sources. Unfortunately, LLMs often struggle with posing the right search queries, especially
Externí odkaz:
http://arxiv.org/abs/2410.23214
Despite its many advantages, the sensible application of the Hybrid Monte Carlo (HMC) method is often hindered by the presence of large - or even infinite - potential barriers. These potential barriers partition the configuration space into distinct
Externí odkaz:
http://arxiv.org/abs/2410.19148
Understanding how the brain processes dynamic natural stimuli remains a fundamental challenge in neuroscience. Current dynamic neural encoding models either take stimuli as input but ignore shared variability in neural responses, or they model this v
Externí odkaz:
http://arxiv.org/abs/2410.16136
Autor:
Zhao, Tony Z., Tompson, Jonathan, Driess, Danny, Florence, Pete, Ghasemipour, Kamyar, Finn, Chelsea, Wahid, Ayzaan
Recent work has shown promising results for learning end-to-end robot policies using imitation learning. In this work we address the question of how far can we push imitation learning for challenging dexterous manipulation tasks. We show that a simpl
Externí odkaz:
http://arxiv.org/abs/2410.13126
Autor:
Mandati, Reddy, Anderson, Vladyslav, Chen, Po-chen, Agarwal, Ankush, Dokic, Tatjana, Barnard, David, Finn, Michael, Cromer, Jesse, Mccauley, Andrew, Tutaj, Clay, Dave, Neha, Besharati, Bobby, Barnett, Jamie, Krall, Timothy
In the past utilities relied on in-field inspections to identify asset defects. Recently, utilities have started using drone-based inspections to enhance the field-inspection process. We consider a vast repository of drone images, providing a wealth
Externí odkaz:
http://arxiv.org/abs/2410.11967
Autor:
Schmidtke, Finn, Vetter, Mathias
Given independent random variables $Y_1, \ldots, Y_n$ with $Y_i \in \{0,1\}$ we test the hypothesis whether the underlying success probabilities $p_i$ are constant or whether they are periodic with an unspecified period length of $r \ge 2$. The test
Externí odkaz:
http://arxiv.org/abs/2410.10203