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MOBISYS
2011
ACM
12 years 9 months ago
Odessa: enabling interactive perception applications on mobile devices
Resource constrained mobile devices need to leverage computation on nearby servers to run responsive applications that recognize objects, people, or gestures from real-time video....
Moo-Ryong Ra, Anmol Sheth, Lily B. Mummert, Padman...
WSDM
2012
ACM
301views Data Mining» more  WSDM 2012»
12 years 1 months ago
Learning evolving and emerging topics in social media: a dynamic nmf approach with temporal regularization
As massive repositories of real-time human commentary, social media platforms have arguably evolved far beyond passive facilitation of online social interactions. Rapid analysis o...
Ankan Saha, Vikas Sindhwani
ICRA
2007
IEEE
155views Robotics» more  ICRA 2007»
14 years 16 days ago
Dogged Learning for Robots
— Ubiquitous robots need the ability to adapt their behaviour to the changing situations and demands they will encounter during their lifetimes. In particular, non-technical user...
Daniel H. Grollman, Odest Chadwicke Jenkins
IWMM
2004
Springer
98views Hardware» more  IWMM 2004»
13 years 11 months ago
Dynamic object sampling for pretenuring
Many state-of-the-art garbage collectors are generational, collecting the young nursery objects more frequently than old objects. These collectors perform well because young objec...
Maria Jump, Stephen M. Blackburn, Kathryn S. McKin...
AGENTS
1999
Springer
13 years 10 months ago
Team-Partitioned, Opaque-Transition Reinforcement Learning
In this paper, we present a novel multi-agent learning paradigm called team-partitioned, opaque-transition reinforcement learning (TPOT-RL). TPOT-RL introduces the concept of usin...
Peter Stone, Manuela M. Veloso