Stochastic networks are a plausible representation of the relational information among entities i... more Stochastic networks are a plausible representation of the relational information among entities in dynamic systems such as living cells or social communities. While there is a rich literature in estimating a static or temporally invariant network from observation data, little has been done toward estimating time-varying networks from time series of entity attributes. In this paper we present two new machine learning methods for estimating time-varying networks, which both build on a temporally smoothed $l_1$-regularized logistic regression formalism that can be cast as a standard convex-optimization problem and solved efficiently using generic solvers scalable to large networks. We report promising results on recovering simulated time-varying networks. For real data sets, we reverse engineer the latent sequence of temporally rewiring political networks between Senators from the US Senate voting records and the latent evolving regulatory networks underlying 588 genes across the life cycle of Drosophila melanogaster from the microarray time course.
Abstract Supervised topic models utilize document's side information for... more Abstract Supervised topic models utilize document's side information for discovering predictive low dimensional representations of docu-ments; and existing models apply likelihood-based estimation. In this paper, we present a max-margin supervised topic model for both ...
Abstract. Building visual recognition models that adapt across different do-mains is a challengin... more Abstract. Building visual recognition models that adapt across different do-mains is a challenging task for computer vision. While feature-learning machines in the form of hierarchial feed-forward models (eg, convolutional neural net-works) showed promise in this direction, they are ...
The Logistic-Normal Topic Admixture Model (LoNTAM), also known as correlated topic model (Blei an... more The Logistic-Normal Topic Admixture Model (LoNTAM), also known as correlated topic model (Blei and Lafferty, 2005), is a promis-ing and expressive admixture-based text model. It can capture topic correlations via the use of a logistic-normal distribu-tion to model non-trivial ...
Stochastic networks are a plausible representation of the relational information among entities i... more Stochastic networks are a plausible representation of the relational information among entities in dynamic systems such as living cells or social communities. While there is a rich literature in estimating a static or temporally invariant network from observation data, little has been done toward estimating time-varying networks from time series of entity attributes. In this paper we present two new machine learning methods for estimating time-varying networks, which both build on a temporally smoothed $l_1$-regularized logistic regression formalism that can be cast as a standard convex-optimization problem and solved efficiently using generic solvers scalable to large networks. We report promising results on recovering simulated time-varying networks. For real data sets, we reverse engineer the latent sequence of temporally rewiring political networks between Senators from the US Senate voting records and the latent evolving regulatory networks underlying 588 genes across the life cycle of Drosophila melanogaster from the microarray time course.
Abstract Supervised topic models utilize document's side information for... more Abstract Supervised topic models utilize document's side information for discovering predictive low dimensional representations of docu-ments; and existing models apply likelihood-based estimation. In this paper, we present a max-margin supervised topic model for both ...
Abstract. Building visual recognition models that adapt across different do-mains is a challengin... more Abstract. Building visual recognition models that adapt across different do-mains is a challenging task for computer vision. While feature-learning machines in the form of hierarchial feed-forward models (eg, convolutional neural net-works) showed promise in this direction, they are ...
The Logistic-Normal Topic Admixture Model (LoNTAM), also known as correlated topic model (Blei an... more The Logistic-Normal Topic Admixture Model (LoNTAM), also known as correlated topic model (Blei and Lafferty, 2005), is a promis-ing and expressive admixture-based text model. It can capture topic correlations via the use of a logistic-normal distribu-tion to model non-trivial ...
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Papers by Amr Ahmed