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» Parameter Space Exploration of Agent-Based Models
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ICML
2004
IEEE
15 years 10 months ago
Parameter space exploration with Gaussian process trees
Computer experiments often require dense sweeps over input parameters to obtain a qualitative understanding of their response. Such sweeps can be prohibitively expensive, and are ...
Robert B. Gramacy, Herbert K. H. Lee, William G. M...
ASPLOS
2006
ACM
15 years 1 months ago
Efficiently exploring architectural design spaces via predictive modeling
Architects use cycle-by-cycle simulation to evaluate design choices and understand tradeoffs and interactions among design parameters. Efficiently exploring exponential-size desig...
Engin Ipek, Sally A. McKee, Rich Caruana, Bronis R...
ACL
1994
14 years 11 months ago
A Markov Language Learning Model for Finite Parameter Spaces
This paper shows how to formally characterize language learning in a finite parameter space as a Markov structure, hnportant new language learning results follow directly: explici...
Partha Niyogi, Robert C. Berwick
EWRL
2008
14 years 11 months ago
Efficient Reinforcement Learning in Parameterized Models: Discrete Parameter Case
We consider reinforcement learning in the parameterized setup, where the model is known to belong to a parameterized family of Markov Decision Processes (MDPs). We further impose ...
Kirill Dyagilev, Shie Mannor, Nahum Shimkin
ISCAS
2008
IEEE
123views Hardware» more  ISCAS 2008»
15 years 4 months ago
Design space exploration of low-phase-noise LC-VCO using multiple-divide technique
— This paper proposes a multiple-divide technique using by-2, by-3, and by-4 frequency dividers to realize a lower phase-noise LC-VCO, and explores the design space of low-phasen...
Shoichi Hara, Takeshi Ito, Kenichi Okada, Akira Ma...