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» Controlling Model Complexity in Flow Estimation
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ICML
2008
IEEE
14 years 9 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
JMIV
2006
124views more  JMIV 2006»
13 years 8 months ago
Segmentation of a Vector Field: Dominant Parameter and Shape Optimization
Vector field segmentation methods usually belong to either of three classes: methods which segment regions homogeneous in direction and/or norm, methods which detect discontinuiti...
Tristan Roy, Eric Debreuve, Michel Barlaud, Gilles...
HAIS
2009
Springer
14 years 10 days ago
Beyond Homemade Artificial Data Sets
One of the most important challenges in supervised learning is how to evaluate the quality of the models evolved by different machine learning techniques. Up to now, we have relied...
Núria Macià, Albert Orriols-Puig, Es...
ICDM
2010
IEEE
212views Data Mining» more  ICDM 2010»
13 years 6 months ago
Modeling Information Diffusion in Implicit Networks
Social media forms a central domain for the production and dissemination of real-time information. Even though such flows of information have traditionally been thought of as diffu...
Jaewon Yang, Jure Leskovec
RSP
2005
IEEE
131views Control Systems» more  RSP 2005»
14 years 2 months ago
Models for Embedded Application Mapping onto NoCs: Timing Analysis
Networks-on-chip (NoCs) are an emergent communication infrastructure, which can be designed to deal with growing system complexity and technology evolution. The efficient use of N...
César A. M. Marcon, Márcio Eduardo K...