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» Optimizing Complex Loss Functions in Structured Prediction
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CVPR
2008
IEEE
14 years 11 months ago
Transfer learning for image classification with sparse prototype representations
To learn a new visual category from few examples, prior knowledge from unlabeled data as well as previous related categories may be useful. We develop a new method for transfer le...
Ariadna Quattoni, Michael Collins, Trevor Darrell
EMNLP
2008
13 years 10 months ago
Stacking Dependency Parsers
We explore a stacked framework for learning to predict dependency structures for natural language sentences. A typical approach in graph-based dependency parsing has been to assum...
André F. T. Martins, Dipanjan Das, Noah A. ...
AAAI
2012
11 years 11 months ago
Transfer Learning with Graph Co-Regularization
Transfer learning proves to be effective for leveraging labeled data in the source domain to build an accurate classifier in the target domain. The basic assumption behind transf...
Mingsheng Long, Jianmin Wang 0001, Guiguang Ding, ...
NAR
2006
79views more  NAR 2006»
13 years 9 months ago
The Database of Macromolecular Motions: new features added at the decade mark
The database of molecular motions, MolMovDB 15 (http://molmovdb.org), has been in existence for the past decade. It classifies macromolecular motions and provides tools to interpo...
Samuel Flores, Nathaniel Echols, Duncan Milburn, B...
BMCBI
2006
102views more  BMCBI 2006»
13 years 9 months ago
MycoperonDB: a database of computationally identified operons and transcriptional units in Mycobacteria
Background: A key post genomics challenge is to identify how genes in an organism come together and perform physiological functions. An important first step in this direction is t...
Sarita Ranjan, Ranjit Kumar Gundu, Akash Ranjan