Zobrazeno 1 - 10
of 2 143
pro vyhledávání: '"Vuorio A"'
Developing autonomous vehicles that can navigate complex environments with human-level safety and efficiency is a central goal in self-driving research. A common approach to achieving this is imitation learning, where agents are trained to mimic huma
Externí odkaz:
http://arxiv.org/abs/2411.04653
In many real-world settings, an agent must learn to act in environments where no reward signal can be specified, but a set of expert demonstrations is available. Imitation learning (IL) is a popular framework for learning policies from such demonstra
Externí odkaz:
http://arxiv.org/abs/2407.00495
A core ambition of reinforcement learning (RL) is the creation of agents capable of rapid learning in novel tasks. Meta-RL aims to achieve this by directly learning such agents. Black box methods do so by training off-the-shelf sequence models end-to
Externí odkaz:
http://arxiv.org/abs/2403.03020
Learning a universal policy across different robot morphologies can significantly improve learning efficiency and enable zero-shot generalization to unseen morphologies. However, learning a highly performant universal policy requires sophisticated ar
Externí odkaz:
http://arxiv.org/abs/2402.06570
Autor:
Jackson, Matthew Thomas, Jiang, Minqi, Parker-Holder, Jack, Vuorio, Risto, Lu, Chris, Farquhar, Gregory, Whiteson, Shimon, Foerster, Jakob Nicolaus
The past decade has seen vast progress in deep reinforcement learning (RL) on the back of algorithms manually designed by human researchers. Recently, it has been shown that it is possible to meta-learn update rules, with the hope of discovering algo
Externí odkaz:
http://arxiv.org/abs/2310.02782
Deep reinforcement learning (RL) is notoriously impractical to deploy due to sample inefficiency. Meta-RL directly addresses this sample inefficiency by learning to perform few-shot learning when a distribution of related tasks is available for meta-
Externí odkaz:
http://arxiv.org/abs/2309.14970
Publikováno v:
Vascular Health and Risk Management, Vol 2014, Iss default, Pp 263-270 (2014)
Alpo Vuorio,1,2 Matti J Tikkanen,3 Petri T Kovanen4 1Health Center Mehiläinen, Vantaa, Finland; 2Finnish Institute of Occupational Health, Lappeenranta, Finland; 3Heart and Lung Center, Helsinki University Central Hospital, Folkhälsan Research Cent
Externí odkaz:
https://doaj.org/article/a065e1c51c154eb58dfca695a9754e8a
Autor:
Johanna Lilja, Jasmin Kaivola, James R. W. Conway, Joni Vuorio, Hanna Parkkola, Pekka Roivas, Michal Dibus, Megan R. Chastney, Taru Varila, Guillaume Jacquemet, Emilia Peuhu, Emily Wang, Ulla Pentikäinen, Itziar Martinez D. Posada, Hellyeh Hamidi, Arafath K. Najumudeen, Owen J. Sansom, Igor L. Barsukov, Daniel Abankwa, Ilpo Vattulainen, Marko Salmi, Johanna Ivaska
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-20 (2024)
Abstract The KRAS oncogene drives many common and highly fatal malignancies. These include pancreatic, lung, and colorectal cancer, where various activating KRAS mutations have made the development of KRAS inhibitors difficult. Here we identify the s
Externí odkaz:
https://doaj.org/article/aab4a06c715b4e4481e20d3e6037ab7e
Autor:
Beck, Jacob, Vuorio, Risto, Liu, Evan Zheran, Xiong, Zheng, Zintgraf, Luisa, Finn, Chelsea, Whiteson, Shimon
While deep reinforcement learning (RL) has fueled multiple high-profile successes in machine learning, it is held back from more widespread adoption by its often poor data efficiency and the limited generality of the policies it produces. A promising
Externí odkaz:
http://arxiv.org/abs/2301.08028
Publikováno v:
Annals of Medicine, Vol 56, Iss 1 (2024)
There is growing concern that the severe respiratory disease in birds (avian influenza or ‘bird flu’) caused by the H5N1 influenza virus, might potentially spread more widely to humans and cause a pandemic. Here we discuss clinical issues related
Externí odkaz:
https://doaj.org/article/af4b8a5d5cf5461697bb150562e973bf