Discovering Latent Strategies

A Bayesian Network Model for Discovering Handwriting Strategies of Primary School Children

In this paper, we present the results of a longitudinal study for the evolution follow-up in writing among typical pupils in primary education. We propose a method aimed at discovering groups of pupils sharing the same handwriting strategies along their primary education. From the on-line acquisition of writing and drawing tests, writing strategies are modeled by means of a bayesian network. Ex...

Discovering causal relations among latent variables in directed acyclic graphical models

Discovering Latent Structures: Experience with the CoIL Challenge 2000 Data Set

We present a case study to demonstrate the possibility of discovering complex and interesting latent structures using hierarchical latent class (HLC) models. A similar effort was made earlier [6], but that study involved only small applications with 4 or 5 observed variables. Due to recent progress in algorithm research, it is now possible to learn HLC models with dozens of observed variables. ...

Probabilistic Community Discovery Using Hierarchical Latent Gaussian Mixture Model

Complex networks exist in a wide array of diverse domains, ranging from biology, sociology, and computer science. These real-world networks, while disparate in nature, often comprise of a set of loose clusters(a.k.a communities), whose members are better connected to each other than to the rest of the network. Discovering such inherent community structures can lead to deeper understanding about...

Transfer Learning by Discovering Latent Task Parametrizations

We present a framework that is able to discover the latent factors that parametrize a family of related tasks from data. The resulting model is able to rapidly identify the dynamics of a new task instance, allowing an agent to flexibly adapt to task variations.