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ICML
2008
IEEE
16 years 8 months ago
On the quantitative analysis of deep belief networks
Deep Belief Networks (DBN's) are generative models that contain many layers of hidden variables. Efficient greedy algorithms for learning and approximate inference have allow...
Ruslan Salakhutdinov, Iain Murray
NECO
2008
170views more  NECO 2008»
15 years 7 months ago
Representational Power of Restricted Boltzmann Machines and Deep Belief Networks
Deep Belief Networks (DBN) are generative neural network models with many layers of hidden explanatory factors, recently introduced by Hinton et al., along with a greedy layer-wis...
Nicolas Le Roux, Yoshua Bengio
TFS
2008
129views more  TFS 2008»
15 years 5 months ago
A Functional-Link-Based Neurofuzzy Network for Nonlinear System Control
Abstract--This study presents a functional-link-based neurofuzzy network (FLNFN) structure for nonlinear system control. The proposed FLNFN model uses a functional link neural netw...
Cheng-Hung Chen, Cheng-Jian Lin, Chin-Teng Lin
COMCOM
2006
100views more  COMCOM 2006»
15 years 7 months ago
Routing and wavelength assignment for core-based tree in WDM networks
In this paper, we address the routing and wavelength assignment problem for the core-based tree (CBT) service in a wavelength-division-multiplexing (WDM) network, where k sources ...
Jianping Wang, Xiangtong Qi, Mei Yang
INFOCOM
2005
IEEE
16 years 24 days ago
Joint optimal scheduling and routing for maximum network throughput
— In this paper we consider packet networks loaded by admissible traffic patterns, i.e. by traffic patterns that, if optimally routed, do not overload network resources. In the...
Emilio Leonardi, Marco Mellia, Marco Ajmone Marsan...