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of 26
pro vyhledávání: '"Dick, Jeffery"'
Autor:
Dick, Jeffery, Nath, Saptarshi, Peridis, Christos, Benjamin, Eseoghene, Kolouri, Soheil, Soltoggio, Andrea
Context detection involves labeling segments of an online stream of data as belonging to different tasks. Task labels are used in lifelong learning algorithms to perform consolidation or other procedures that prevent catastrophic forgetting. Inferrin
Externí odkaz:
http://arxiv.org/abs/2405.19047
Autor:
Soltoggio, Andrea, Ben-Iwhiwhu, Eseoghene, Peridis, Christos, Ladosz, Pawel, Dick, Jeffery, Pilly, Praveen K., Kolouri, Soheil
This paper introduces a set of formally defined and transparent problems for reinforcement learning algorithms with the following characteristics: (1) variable degrees of observability (non-Markov observations), (2) distal and sparse rewards, (3) var
Externí odkaz:
http://arxiv.org/abs/2302.10887
Autor:
Ben-Iwhiwhu, Eseoghene, Dick, Jeffery, Ketz, Nicholas A., Pilly, Praveen K., Soltoggio, Andrea
Publikováno v:
Neural Networks 2022
Meta-reinforcement learning (meta-RL) algorithms enable agents to adapt quickly to tasks from few samples in dynamic environments. Such a feat is achieved through dynamic representations in an agent's policy network (obtained via reasoning about task
Externí odkaz:
http://arxiv.org/abs/2111.00134
Autor:
Dey, Jayanta, Geisa, Ali, Mehta, Ronak, Tomita, Tyler M., Helm, Hayden S., Xu, Haoyin, Eaton, Eric, Dick, Jeffery, Priebe, Carey E., Vogelstein, Joshua T.
Learning is a process wherein a learning agent enhances its performance through exposure of experience or data. Throughout this journey, the agent may encounter diverse learning environments. For example, data may be presented to the leaner all at on
Externí odkaz:
http://arxiv.org/abs/2109.14501
Autor:
Ben-Iwhiwhu, Eseoghene, Ladosz, Pawel, Dick, Jeffery, Chen, Wen-Hua, Pilly, Praveen, Soltoggio, Andrea
Rapid online adaptation to changing tasks is an important problem in machine learning and, recently, a focus of meta-reinforcement learning. However, reinforcement learning (RL) algorithms struggle in POMDP environments because the state of the syste
Externí odkaz:
http://arxiv.org/abs/2004.12846
Autor:
Ladosz, Pawel, Ben-Iwhiwhu, Eseoghene, Dick, Jeffery, Hu, Yang, Ketz, Nicholas, Kolouri, Soheil, Krichmar, Jeffrey L., Pilly, Praveen, Soltoggio, Andrea
This paper presents a new neural architecture that combines a modulated Hebbian network (MOHN) with DQN, which we call modulated Hebbian plus Q network architecture (MOHQA). The hypothesis is that such a combination allows MOHQA to solve difficult pa
Externí odkaz:
http://arxiv.org/abs/1909.09902
Autor:
Ben-Iwhiwhu, Eseoghene, Dick, Jeffery, Ketz, Nicholas A., Pilly, Praveen K., Soltoggio, Andrea
Publikováno v:
In Neural Networks August 2022 152:70-79
Autor:
Geisa, Ali, Mehta, Ronak, Helm, Hayden S., Dey, Jayanta, Eaton, Eric, Dick, Jeffery, Priebe, Carey E., Vogelstein, Joshua T.
What is learning? 20$^{st}$ century formalizations of learning theory -- which precipitated revolutions in artificial intelligence -- focus primarily on $\mathit{in-distribution}$ learning, that is, learning under the assumption that the training dat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9c47d06b4286a9f2ddd1717d512fd4f3
http://arxiv.org/abs/2109.14501
http://arxiv.org/abs/2109.14501
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