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NECO
2008
170views more  NECO 2008»
13 years 6 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
ICMLA
2009
13 years 4 months ago
Learning Deep Neural Networks for High Dimensional Output Problems
State-of-the-art pattern recognition methods have difficulty dealing with problems where the dimension of the output space is large. In this article, we propose a new framework ba...
Benjamin Labbé, Romain Hérault, Cl&e...
TOOLS
2010
IEEE
13 years 11 months ago
Deep Meta-modelling with MetaDepth
Meta-modelling is at the core of Model-Driven Engineering, where it is used for language engineering and domain modelling. The OMG’s Meta-Object Facility is the standard framewor...
Juan de Lara, Esther Guerra
ISCAS
1995
IEEE
107views Hardware» more  ISCAS 1995»
13 years 10 months ago
Power Dissipation in Deep Submicron CMOS Digital Circuits
— This paper introduces a simple analytical model for estimating standby and switching power dissipation in deep submicron CMOS digital circuits. The model is based on Berkeley S...
R. X. Gu, Mohamed I. Elmasry
INFOCOM
2012
IEEE
11 years 9 months ago
FlowSifter: A counting automata approach to layer 7 field extraction for deep flow inspection
Abstract—In this paper, we introduce FlowSifter, a systematic framework for online application protocol field extraction. FlowSifter introduces a new grammar model Counting Regu...
Chad R. Meiners, Eric Norige, Alex X. Liu, Eric To...