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ML
2002
ACM
154views Machine Learning» more  ML 2002»
13 years 7 months ago
Technical Update: Least-Squares Temporal Difference Learning
TD() is a popular family of algorithms for approximate policy evaluation in large MDPs. TD() works by incrementally updating the value function after each observed transition. It h...
Justin A. Boyan
DSN
2008
IEEE
14 years 2 months ago
Scheduling algorithms for unpredictably heterogeneous CMP architectures
In future large-scale multi-core microprocessors, hard errors and process variations will create dynamic heterogeneity, causing performance and power characteristics to differ amo...
Jonathan A. Winter, David H. Albonesi
GPC
2007
Springer
13 years 9 months ago
Towards a Peer-To-Peer Platform for High Performance Computing
This paper describes a Global Computing (GC) environment, called XtremWeb-CH (XWCH). XWCH is an improved version of a GC tool called XtremWeb (XW). XWCH tries to enrich XW in orde...
Nabil Abdennadher, Régis Boesch
COMSWARE
2007
IEEE
14 years 2 months ago
On Optimal Performance in Mobile Ad hoc Networks
In this paper we are concerned with finding the maximum throughput that a mobile ad hoc network can support. Even when nodes are stationary, the problem of determining the capaci...
Tapas K. Patra, Joy Kuri, Pavan Nuggehalli
ANCS
2007
ACM
13 years 12 months ago
Experimental evaluation of a coarse-grained switch scheduler
Modern high performance routers rely on sophisticated interconnection networks to meet ever increasing demands on capacity. Previous studies have used a combination of analysis an...
Charlie Wiseman, Jonathan S. Turner, Ken Wong, Bra...