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[PDF] Top 20 A Comparison of Latent Variable Models For Conversation Analysis

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A Comparison of Latent Variable Models For Conversation Analysis

A Comparison of Latent Variable Models For Conversation Analysis

... the conversation as well as a context specific language ...the models on the tasks of identify- ing the set of people present in any conver- sation, as well as identifying the speaker for every utterance in ... See full document

9

Learning Latent Variable Models by Pairwise Cluster Comparison: Part I - Theory and Overview

Learning Latent Variable Models by Pairwise Cluster Comparison: Part I - Theory and Overview

... with latent variables and clustering ...graphical models with clustering. According to the pairwise cluster comparison (PCC) concept, we compare pairwise clusters of data points representing ... See full document

52

Learning Latent Variable Models by Pairwise Cluster Comparison: Part II - Algorithm and Evaluation

Learning Latent Variable Models by Pairwise Cluster Comparison: Part II - Algorithm and Evaluation

... theoretical models from the literature and to the out- puts of four state-of-the-art learning ...MIM models, we did not use it for the other data ...HLC models (Zhang, 2004), and since the ... See full document

45

Bayesian Analysis of Linear and Nonlinear Latent Variable Models with Fixed Covariate and Ordered Categorical Data

Bayesian Analysis of Linear and Nonlinear Latent Variable Models with Fixed Covariate and Ordered Categorical Data

... In this paper, ordered categorical variables are used to compare between linear with covariate and nonlinear interactions of covariates and latent variables in Bayesian structural equation models. Gibbs ... See full document

16

Posterior Regularization for Structured Latent Variable Models

Posterior Regularization for Structured Latent Variable Models

... For example, in the alignment at the top right of Figure 5, the posteriors over the source word schism clearly sum to more than 1. The effect of applying PR constraints to the posteriors is shown in the second row. ... See full document

49

Learning the Structure of Linear Latent Variable Models

Learning the Structure of Linear Latent Variable Models

... is using the approximation derived by Kano and Harada (2000) to rapidly calculate the fitness of a factor analysis model when a variable is removed. Another alternative is a greedy search over the initial ... See full document

56

Implicit Deep Latent Variable Models for Text Generation

Implicit Deep Latent Variable Models for Text Generation

... Datasets. Following (Shen et al., 2017), the Yelp restaurant reviews dataset is processed from the original Yelp dataset in language modeling. Re- views with user rating above three are considered positive, and those ... See full document

11

Unsupervised Learning with Contrastive Latent Variable Models

Unsupervised Learning with Contrastive Latent Variable Models

... further analysis. To learn a latent representation that is only a function of a subset of the observed dimensions, certain rows of the target factor loading, W, must be ...the latent representation ... See full document

8

Latent variable mixture models to test for differential item functioning: a population-based analysis

Latent variable mixture models to test for differential item functioning: a population-based analysis

... multiple-class models, we performed analyses by fitting models with the same number of classes but with different numbers of random starts ...the models with different sets of random starts were very ... See full document

13

Online Tensor Methods for Learning Latent Variable Models

Online Tensor Methods for Learning Latent Variable Models

... 2008) models and other latent variable ...theoretical analysis of the method, the current paper considers a careful implementation of the ... See full document

39

Penalized Latent Variable Estimator For Finite Mixture of Regression Models.

Penalized Latent Variable Estimator For Finite Mixture of Regression Models.

... To compare the performance of the model selection techniques, a few measures are calculated. First, the AIC and BIC criterion are calculated and used to tune the penalty parameters for each method. The mean squared ... See full document

73

Building Blocks for Variational Bayesian Learning of Latent Variable Models

Building Blocks for Variational Bayesian Learning of Latent Variable Models

... An astrophysical problem of separating young and old star populations from a set of elliptical galaxy spectra was studied by one of the authors in Nolan et al. (2006). Since the observed quantities were energies and thus ... See full document

47

Perturbative Corrections for Approximate Inference in Gaussian Latent Variable Models

Perturbative Corrections for Approximate Inference in Gaussian Latent Variable Models

... The argument goes back to the mathematical analysis of the Sherrington-Kirkpatrick (SK) model for a disordered magnet (a so-called spin glass) (Sherrington and Kirckpatrick, 1975). For this Ising model, the ... See full document

42

Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models

Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models

... probabilistic latent variable ...the latent projections, X, to the observed data, ...the latent-space and the latent-space’s posterior distribution is approximated by ...the ... See full document

34

Latent Variable Models for Semantic Orientations of Phrases

Latent Variable Models for Semantic Orientations of Phrases

... computational models for phrases with semantic orientations as well as classification methods based on the ...affect analysis of higher linguistic units such as sentences and doc- ... See full document

8

Semi supervised latent variable models for sentence level sentiment analysis

Semi supervised latent variable models for sentence level sentiment analysis

... ditional models can handle rich and overlapping fea- tures and allow for exact inference and simple gradi- ent based ...former models are largely orthogonal to the one we propose in this work and combining ... See full document

6

Integrative Analysis using Coupled Latent Variable Models for Individualizing Prognoses

Integrative Analysis using Coupled Latent Variable Models for Individualizing Prognoses

... that models the future trajectory of a single target clinical marker tracking a disease process of ...for latent factors influencing disease expression, whereas standard regres- sion models rely on ... See full document

35

Latent Variable Models of Concept Attribute Attachment

Latent Variable Models of Concept Attribute Attachment

... standard models such as Latent Semantic Analysis (Hofmann, 1999), im- proving annotation precision; (3) Attributes with multiple related meanings ...car models), then that attribute might ... See full document

9

Latent variable models for binary response data

Latent variable models for binary response data

... We apply and compare both methods by contouring the values for Lg as a function of aj_ ^ and as defined above, using the subroutine library GINO-SURF. This is done using 3 sets of data that have been analysed in ... See full document

356

Robustness in Latent Variable Models

Robustness in Latent Variable Models

... the analysis of car- diovascular disease outcomes in the Framingham study (Kannel et ...a latent variable with some distribution in the population of subjects, information on which is only available ... See full document

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