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» Learning words from sights and sounds: a computational model
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125
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JCB
2008
159views more  JCB 2008»
14 years 9 months ago
BayesMD: Flexible Biological Modeling for Motif Discovery
We present BayesMD, a Bayesian Motif Discovery model with several new features. Three different types of biological a priori knowledge are built into the framework in a modular fa...
Man-Hung Eric Tang, Anders Krogh, Ole Winther
CUZA
2002
129views more  CUZA 2002»
14 years 9 months ago
Ad Hoc Metacomputing with Compeer
Metacomputing allows the exploitation of geographically seperate, heterogenous networks and resources. Most metacomputers are feature rich and carry a long, complicated installati...
Keith Power, John P. Morrison
SIAMIS
2010
378views more  SIAMIS 2010»
14 years 4 months ago
Global Interactions in Random Field Models: A Potential Function Ensuring Connectedness
Markov random field (MRF) models, including conditional random field models, are popular in computer vision. However, in order to be computationally tractable, they are limited to ...
Sebastian Nowozin, Christoph H. Lampert
99
Voted
ICCV
2007
IEEE
15 years 3 months ago
Total Recall: Automatic Query Expansion with a Generative Feature Model for Object Retrieval
Given a query image of an object, our objective is to retrieve all instances of that object in a large (1M+) image database. We adopt the bag-of-visual-words architecture which ha...
Ondrej Chum, James Philbin, Josef Sivic, Michael I...
JCB
2006
185views more  JCB 2006»
14 years 9 months ago
A Probabilistic Methodology for Integrating Knowledge and Experiments on Biological Networks
Biological systems are traditionally studied by focusing on a specific subsystem, building an intuitive model for it, and refining the model using results from carefully designed ...
Irit Gat-Viks, Amos Tanay, Daniela Raijman, Ron Sh...