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» Greedy in Approximation Algorithms
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135
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NECO
2008
170views more  NECO 2008»
15 years 2 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
138
Voted
SIAMSC
2010
145views more  SIAMSC 2010»
14 years 9 months ago
An "hp" Certified Reduced Basis Method for Parametrized Elliptic Partial Differential Equations
We present a new "hp" parameter multi-domain certified reduced basis method for rapid and reliable online evaluation of functional outputs associated with parametrized el...
Jens L. Eftang, Anthony T. Patera, Einar M. R&osla...
113
Voted
ICPP
2005
IEEE
15 years 8 months ago
Filter Decomposition for Supporting Coarse-Grained Pipelined Parallelism
We consider the filter decomposition problem in supporting coarse-grained pipelined parallelism. This form of parallelism is suitable for data-driven applications in scenarios wh...
Wei Du, Gagan Agrawal
129
Voted
VISUALIZATION
1995
IEEE
15 years 6 months ago
Automatic Generation of Triangular Irregular Networks Using Greedy Cuts
We propose a new approach to the automatic generation of triangular irregular networks from dense terrain models. We have developed and implemented an algorithm based on the greed...
Cláudio T. Silva, Joseph S. B. Mitchell, Ar...
CDC
2010
IEEE
128views Control Systems» more  CDC 2010»
14 years 9 months ago
Greedy sensor selection: Leveraging submodularity
ACT We consider the problem of sensor selection in resource constrained sensor networks. The fusion center selects a subset of k sensors from an available pool of m sensors accordi...
Manohar Shamaiah, Siddhartha Banerjee, Haris Vikal...