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AAAI
2011
14 years 6 months ago
Coarse-to-Fine Inference and Learning for First-Order Probabilistic Models
Coarse-to-fine approaches use sequences of increasingly fine approximations to control the complexity of inference and learning. These techniques are often used in NLP and visio...
Chloe Kiddon, Pedro Domingos
KDD
2007
ACM
167views Data Mining» more  KDD 2007»
16 years 7 months ago
Generalized component analysis for text with heterogeneous attributes
We present a class of richly structured, undirected hidden variable models suitable for simultaneously modeling text along with other attributes encoded in different modalities. O...
Xuerui Wang, Chris Pal, Andrew McCallum
BMCBI
2006
123views more  BMCBI 2006»
15 years 6 months ago
Computational models with thermodynamic and composition features improve siRNA design
Background: Small interfering RNAs (siRNAs) have become an important tool in cell and molecular biology. Reliable design of siRNA molecules is essential for the needs of large fun...
Svetlana A. Shabalina, Alexey N. Spiridonov, Aleks...
SIGIR
2009
ACM
16 years 1 months ago
Temporal collaborative filtering with adaptive neighbourhoods
Recommender Systems, based on collaborative filtering (CF), aim to accurately predict user tastes, by minimising the mean error achieved on hidden test sets of user ratings, afte...
Neal Lathia, Stephen Hailes, Licia Capra
BMCBI
2006
116views more  BMCBI 2006»
15 years 6 months ago
A model-based approach to selection of tag SNPs
Background: Single Nucleotide Polymorphisms (SNPs) are the most common type of polymorphisms found in the human genome. Effective genetic association studies require the identific...
Pierre Nicolas, Fengzhu Sun, Lei M. Li