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pro vyhledávání: '"Yee, Jeremy"'
Autor:
Yee, Jeremy Stephen Gabriel, Ng, Pai Chet, Wang, Zhengkui, McLoughlin, Ian, Ng, Aik Beng, See, Simon
This paper presents a systematic review of the infrastructure requirements for deploying Large Language Models (LLMs) on-device within the context of small and medium-sized enterprises (SMEs), focusing on both hardware and software perspectives. From
Externí odkaz:
http://arxiv.org/abs/2410.16070
Autor:
Yee, Jeremy
This paper studies value iteration for infinite horizon contracting Markov decision processes under convexity assumptions and when the state space is uncountable. The original value iteration is replaced with a more tractable form and the fixed point
Externí odkaz:
http://arxiv.org/abs/1802.07243
Autor:
Hinz, Juri, Yee, Jeremy
This short paper gives an introduction to the \emph{rcss} package. The R package \emph{rcss} provides users with a tool to approximate the value functions in the Bellman recursion using convex piecewise linear functions formed using operations on tan
Externí odkaz:
http://arxiv.org/abs/1801.06029
Autor:
Yee, Jeremy
This short paper briefly describes the implementation of the least squares Monte Carlo method in the rlsm package. This package provides users with an easy manner to experiment with the large amount of R regression tools on any regression basis and r
Externí odkaz:
http://arxiv.org/abs/1801.05554
Autor:
Yee, Jeremy
This paper studies function approximation for finite horizon discrete time Markov decision processes under certain convexity assumptions. Uniform convergence of these approximations on compact sets is proved under several sampling schemes for the dri
Externí odkaz:
http://arxiv.org/abs/1712.00970
Autor:
Hinz, Juri, Yee, Jeremy
The increased market penetration of renewable energy sources and the rapid development of electric battery storage technologies yield a potential for reducing electricity price volatility while maintaining stability of the power grid. This work prese
Externí odkaz:
http://arxiv.org/abs/1706.03310
Complexity and uncertainty associated with commodity resource valuation and extraction requires stochastic control methods suitable for high dimensional states. Recent progress in duality and trajectory-wise techniques has introduced a variety of fre
Externí odkaz:
http://arxiv.org/abs/1602.00210
Autor:
Hinz, Juri1 Juri.Hinz@uts.edu.au, Yee, Jeremy1 jeremyyee@outlook.com.au
Publikováno v:
R Journal. Dec2018, Vol. 10 Issue 2, p38-54. 17p.
Akademický článek
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Autor:
Yee, Jeremy Wei Xian
University of Technology Sydney. Faculty of Science. This thesis presents a subgradient and duality approach towards solving an important class of Markov decision processes. The key assumptions lie in the linear state dynamics and in the convexity of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______363::18ef9f06a385a4295d55e20912bd76d5
https://hdl.handle.net/10453/129439
https://hdl.handle.net/10453/129439