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JMLR
2010
151views more  JMLR 2010»
13 years 4 months ago
Understanding the difficulty of training deep feedforward neural networks
Whereas before 2006 it appears that deep multilayer neural networks were not successfully trained, since then several algorithms have been shown to successfully train them, with e...
Xavier Glorot, Yoshua Bengio
IJCNN
2008
IEEE
14 years 3 months ago
Uncertainty propagation for quality assurance in Reinforcement Learning
— In this paper we address the reliability of policies derived by Reinforcement Learning on a limited amount of observations. This can be done in a principled manner by taking in...
Daniel Schneegaß, Steffen Udluft, Thomas Mar...
JSAC
2010
188views more  JSAC 2010»
13 years 4 months ago
Random-walk based approach to detect clone attacks in wireless sensor networks
Abstract--Wireless sensor networks (WSNs) deployed in hostile environments are vulnerable to clone attacks. In such attack, an adversary compromises a few nodes, replicates them, a...
Yingpei Zeng, Jiannong Cao, Shigeng Zhang, Shanqin...
ICDM
2009
IEEE
205views Data Mining» more  ICDM 2009»
14 years 4 months ago
Active Selection of Sensor Sites in Remote Sensing Applications
— In a data-mining approach, a model for estimation of Aerosol Optical Depth (AOD) from satellite observations is learned using collocated satellite and groundbased observations....
Debasish Das, Zoran Obradovic, Slobodan Vucetic
ICONIP
2010
13 years 7 months ago
Improving Recurrent Neural Network Performance Using Transfer Entropy
Abstract. Reservoir computing approaches have been successfully applied to a variety of tasks. An inherent problem of these approaches, is, however, their variation in performance ...
Oliver Obst, Joschka Boedecker, Minoru Asada