Markov Random Field Based Automatic Image Alignment for Electron Tomography (2007)
Moussavi, Farshid, Amat, Fernando, Comolli, Luis R., Elidan, Gal, Downing, Kenneth H., Horowitz, Mark
The Information Bottleneck EM Algorithm (2003)
Learning with hidden variables is a central challenge in probabilistic graphical models that has important implications for many real-life problems. The classical approach is using the Expectation...
Data Perturbation for Escaping Local Maxima in Learning (2003)
Gal Elidan, Matan Ninio, Nir Friedman, Dale Schuurmans
Almost all machine learning algorithms---be they for regression, classification or density estimation---seek hypotheses that optimize a score on training data. In most interesting cases, however,...
The Information Bottleneck EM Algorithm (2003)
Learning with hidden variables is a central challenge in probabilistic graphical models that has important implications for many real-life problems. The classical approach is using the Expectation...
Modeling Dependencies in Protein-DNA Binding Sites (2003)
Yoseph Barash, Gal Elidan, Nir Friedman, Tommy Kaplan
The availability of whole genome sequences and high-throughput genomic assays opens the door for in silico analysis of transcription regulation. This includes methods for discovering and...
The Information Bottleneck EM Algorithm (2003)
Learning with hidden variables is a central challenge in probabilistic graphical models that has important implications for many real-life problems. The classical approach is using the Expectation...
Modeling Dependencies in Protein-DNA Binding Sites (2003)
Yoseph Barash, Gal Elidan, Nir Friedman, Tommy Kaplan
The availability of whole genome sequences and high-throughput genomic assays opens the door for in silico analysis of transcription regulation. This includes methods for discovering and...
Genome-wide expression profiles of genetic mutants provide a wide variety of measurements of cellular responses to perturbations. Typical analysis of such data identifies genes affected by...
Data Perturbation for Escaping Local Maxima in Learning (2002)
Gal Elidan, Matan Ninio, Nir Friedman, Dale Schuurmans
Almost all machine learning algorithms -- be they for regression, classification or density estimation -- seek hypotheses that optimize a score on training data. In most interesting cases, however,...
Genome-wide expression profiles of genetic mutants provide a wide variety of measurements of cellular responses to perturbations. Typical analysis of such data identifies genes affected by...
Discovering Hidden Variables: A Structure-Based Approach (2001)
Gal Elidan, Noam Lotner, Nir Friedman, Daphne Koller
A serious problem in learning probabilistic models is the presence of hidden variables. These variables are not observed, yet interact with several of the observed variables. As such, they induce...
Learning the Dimensionality of Hidden Variables (2001)
A serious problem in learning probabilistic models is the presence of hidden variables. These variables are not observed, yet interact with several of the observed variables. Detecting hidden...
Learning the Dimensionality of Hidden Variables (2001)
A serious problem in learning probabilistic models
Inferring Subnetworks from Perturbed Expression Profiles (2001)
Genome-wide expression profiles of genetic mutants provide a wide variety of measurements of cellular responses to perturbations. Typical analysis of such data identifies genes affected by...
Inferring Subnetworks from Perturbed Expression Profiles (2001)
Genome-wide expression profiles of genetic mutants provide a wide variety of measurements of cellular responses to perturbations. Typical analysis of such data identifies genes affected by...
Inferring Subnetworks from Perturbed Expression Profiles (2001)
Genome-wide expression profiles of genetic mutants provide a wide variety of transcripts measuring the response of cells to perturbations. Standard analysis of such data identifies genes that were...
Discovering Hidden Variables: A Structure-Based Approach (2001)
Gal Elidan, Noam Lotner, Nir Friedman, Daphne Koller
A serious problem in learning probabilistic models is the presence of hidden variables. These variables are not observed, yet interact with several of the observed variables. As such, they induce...
Discovering Hidden Variables: (2001)
Gal Elidan, Noam Lotner, Nir Friedman, Daphne Koller
A serious problem in learning probabilistic models is the presence of hidden variables. These variables are not observed, yet interact with several of the observed variables. As such, they induce...
Discovering Hidden Variables: A Structure-Based Approach (2000)
Gal Elidan, Noam Lotner, Nir Friedman, Daphne Koller
A serious problem in learning probabilistic models is the presence of hidden variables. These variables are not observed, yet interact with several of the observed variables. As such, they induce...