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JCP
2008
126views more  JCP 2008»
15 years 5 months ago
Content-Based Computer Simulation of a Networking Course: An Assessment
This paper presents a successful lab simulation experience to teach signal modulation and demodulation concepts in communication and computer networks to computer science and compu...
Giti Javidi, Ehsan Sheybani
IJON
2007
85views more  IJON 2007»
15 years 5 months ago
Desynchronized stable states in diluted neural networks
The dynamical properties of a diluted fully inhibitory network of pulse-coupled neurons are investigated. Depending on the coupling strength, two different phases can be observed....
Rüdiger Zillmer, Roberto Livi, Antonio Politi...
190
Voted
ML
2007
ACM
192views Machine Learning» more  ML 2007»
15 years 5 months ago
Annealing stochastic approximation Monte Carlo algorithm for neural network training
We propose a general-purpose stochastic optimization algorithm, the so-called annealing stochastic approximation Monte Carlo (ASAMC) algorithm, for neural network training. ASAMC c...
Faming Liang
VTC
2010
IEEE
259views Communications» more  VTC 2010»
15 years 4 months ago
User Multiplexing in Relay Enhanced LTE-Advanced Networks
—The 3rd Generation Partnership Project has recently started the standardization process for LTE-Advanced, as a major evolution step of UTRAN LTE Release 8. One of the key enhanc...
Oumer M. Teyeb, Frank Frederiksen, Vinh Van Phan, ...
CORR
2010
Springer
163views Education» more  CORR 2010»
15 years 4 months ago
Faster Rates for training Max-Margin Markov Networks
Structured output prediction is an important machine learning problem both in theory and practice, and the max-margin Markov network (M3 N) is an effective approach. All state-of-...
Xinhua Zhang, Ankan Saha, S. V. N. Vishwanathan