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AAAI
2008
13 years 10 months ago
Adaptive Importance Sampling with Automatic Model Selection in Value Function Approximation
Off-policy reinforcement learning is aimed at efficiently reusing data samples gathered in the past, which is an essential problem for physically grounded AI as experiments are us...
Hirotaka Hachiya, Takayuki Akiyama, Masashi Sugiya...
NECO
2010
154views more  NECO 2010»
13 years 6 months ago
Role of Homeostasis in Learning Sparse Representations
Neurons in the input layer of primary visual cortex in primates develop edge-like receptive fields. One approach to understanding the emergence of this response is to state that ...
Laurent U. Perrinet
COMCOM
2010
118views more  COMCOM 2010»
13 years 5 months ago
Mini-slot scheduling for IEEE 802.16d chain and grid mesh networks
This work considers the mini-slot scheduling problem in IEEE 802.16d wireless mesh networks (WMNs). An efficient mini-slot scheduling needs to take into account the transmission o...
Jia-Ming Liang, Ho-Cheng Wu, Jen-Jee Chen, Yu-Chee...
PAMI
2012
11 years 10 months ago
CPMC: Automatic Object Segmentation Using Constrained Parametric Min-Cuts
—We present a novel framework to generate and rank plausible hypotheses for the spatial extent of objects in images using bottom-up computational processes and mid-level selectio...
João Carreira, Cristian Sminchisescu
SIGMOD
2007
ACM
197views Database» more  SIGMOD 2007»
14 years 8 months ago
Automated and on-demand provisioning of virtual machines for database applications
Utility computing delivers compute and storage resources to applications as an `on-demand utility', much like electricity, from a distributed collection of computing resource...
Piyush Shivam, Azbayar Demberel, Pradeep Gunda, Da...