Latent Variable Generation using Intermediate Models
Implicit Deep Latent Variable Models for Text Generation
... Deep latent variable models (LVM) such as variational auto-encoder (VAE) have recently played an important role in text ...smooth latent structures to guide the ...implicit latent ...
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Robustness in Latent Variable Models
... in Latent Variable ...Statistical models involving latent variables are widely used in many areas of applications, such as biomedical science and social ...the latent variables are ...
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Applying Conditional Distributions To Individuals: Using Latent Variable Models
... the randomly-generated weights perform equally well as the models with clinicians’ judgements. Meehl (1996) pointed out the reason behind this situation is that the human brain is not designed for optimizing, ...
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Approximation methods for latent variable models
... In between these two extremes, perhaps a range of component sizes that can be approximated with varying accuracy could be considered for undertaking any infer- ence. This particular choice is investigated in chapter 3. ...
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Open Domain Event Extraction Using Neural Latent Variable Models
... 4 Institute of Advanced Technology, Westlake Institute for Advanced Study [email protected] Abstract We consider open domain event extraction, the task of extracting unconstraint types of events from news clusters. A ...
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Characterizing Spatiotemporal Trends in Amphibian Abundance Using Latent Variable Models.
... populations at sites close together, but in separate states should exhibit vastly different patterns compared to populations in the same state that are separated by a much greater distance. There are exceptions to this ...
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Childhood malnutrition in Egypt using geoadditive gaussian and latent variable models
... a latent variable model (LVM) for the nutritional status based on continuous indicators is ...the latent variables through a flexible geoadditive ...geoadditive latent variable ...
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Estimation of Dynamic Latent Variable Models Using Simulated Nonparametric Moments
... 2.3.4. Computational issues. Estimation of a complicated model using long simulation may become computationally burdensome, since kernel smoothing is a computationally intensive procedure. Another problem is ...
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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 ...predictions using the histories of many related clinical markers as ...for latent factors ...
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Probabilistic Distributional Semantics with Latent Variable Models
... Modifies: manager, asset, raiser, statement, management, commissioner Modified by: nature, pension, international, monetary, national, social, trust Prepositional: for nature, contribution to, for investment, for ...
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Nonconvex Global Optimization for Latent Variable Models
... one and the parses can consist of fractional depen- dency edges. We project in order to compute the true objective and compare with other solutions. Our results demonstrate that our method can ob- tain higher likelihoods ...
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Latent Variable Models for Semantic Orientations of Phrases
... We succeeded in addressing the data-sparseness problem by introducing a latent variable. How- ever, this problem still causes some errors. Pre- cise statistics cannot be obtained for infrequent words. This ...
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Latent variable models for binary response data
... above, using the subroutine library GINO-SURF. This is done using 3 sets of data that have been analysed in Bartholomew (1987, Chapter 9), using a single latent variable logit/probit ...
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SILVar: Single Index Latent Variable Models
... that using only the first two singular vectors, the performance is fairly ...Surprisingly, using the top 5 or 6 singular vectors achieves performance greater than that of the full ...classification ...
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Latent Variable Models of Concept Attribute Attachment
... DRR scores for the subset are given in the lower half of Table 2. Averaged over all gold test at- tributes, DRR scores double when using fsLDA. These results can be misleading, however, due to artificially low ...
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Estimation of generalized linear latent variable models
... use models based on theoretical concepts that cannot be observed ...theoretical models. However, when these models are validated using observed data, the problem of measurement ...
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A Comparison of Latent Variable Models For Conversation Analysis
... The large state space in this model makes compu- tation extremely expensive. However, an examina- tion of the posterior probabilities show that a number of states are rarely, or never, entered. We prune away such states ...
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Priors for Diversity in Generative Latent Variable Models
... Probabilistic latent variable models are one of the cornerstones of machine learn- ...Such models are useful for exploratory analysis and visual- ization, for building density models of ...
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Discriminative latent variable models for visual recognition
... In [9], a dataset is built where the joint positions of each human image are labeled in 3D space via a 2D-3D lifting procedure. We simply annotate the joint positions of human body in the 2D image space, as shown in Fig. ...
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Latent variable models for mixed manifest variables
... Bock and Lieberman (1970) fitted a response model on a number of binary items using an unconditional maximum likelihood estimation of a two parameter probit model on the assumption tha[r] ...
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