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NN
2010
Springer
187views Neural Networks» more  NN 2010»
14 years 9 months ago
Efficient exploration through active learning for value function approximation in reinforcement learning
Appropriately designing sampling policies is highly important for obtaining better control policies in reinforcement learning. In this paper, we first show that the least-squares ...
Takayuki Akiyama, Hirotaka Hachiya, Masashi Sugiya...
VLSID
2008
IEEE
153views VLSI» more  VLSID 2008»
16 years 2 months ago
Total Power Minimization in Glitch-Free CMOS Circuits Considering Process Variation
Compared to subthreshold leakage, dynamic power is normally much less sensitive to the process variation due to its approximately linear relation to the process parameters. Howeve...
Yuanlin Lu, Vishwani D. Agrawal
NIPS
2004
15 years 3 months ago
Log-concavity Results on Gaussian Process Methods for Supervised and Unsupervised Learning
Log-concavity is an important property in the context of optimization, Laplace approximation, and sampling; Bayesian methods based on Gaussian process priors have become quite pop...
Liam Paninski
131
Voted
AUTOMATICA
2007
97views more  AUTOMATICA 2007»
15 years 2 months ago
Control of dynamic keyhole welding process
Weld joint penetration control is a basic research topic in the welding research community. The authors propose using an innovative plasma arc welding process referred to as the q...
Y. M. Zhang, Y. C. Liu
NIPS
2004
15 years 3 months ago
Using the Equivalent Kernel to Understand Gaussian Process Regression
The equivalent kernel [1] is a way of understanding how Gaussian process regression works for large sample sizes based on a continuum limit. In this paper we show (1) how to appro...
Peter Sollich, Christopher K. I. Williams