latent variable 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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Dynamical Binary Latent Variable Models for 3D Human Pose Tracking
... current latent variable mod- els are derived from the Gaussian Process Latent Variable Model [8, 23] and the Gaussian Process Dynamical Model ...Such models can serve as effective ...
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Overcoming the Memory Bottleneck in Distributed Training of Latent Variable Models of Text
... unsupervised latent variable models (LVMs) of text, such as Latent Dirichlet Al- location models or Hidden Markov Models (HMMs), are constructed using parallel train- ing ...
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Online but Accurate Inference for Latent Variable Models with Local Gibbs Sampling
... large-scale latent variable ...for latent variable models from a non-canonical exponential family, and draw explicit links between several previously proposed frequentist or Bayesian ...
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Probabilistic Distributional Semantics with Latent Variable Models
... “parametric” latent variable models used here the number of topics or semantic classes, | Z | , must be fixed in ...of models trained with different choices of | Z | ; this avoids the need to ...
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Latent variable models for binary response data
... in latent variable models, since they involve observable (manifest) variables and not directly observable (latent) variables which corresponds to a case of incomplete data, as defined by ...
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Particle methods for maximum likelihood estimation in latent variable models
... Abstract. Standard methods for maximum likelihood parameter estimation in latent variable models rely on the Expectation-Maximization algorithm and its Monte Carlo variants. Our approach is different ...
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Integrative Analysis using Coupled Latent Variable Models for Individualizing Prognoses
... marker-specific latent variable models to neatly summarize and ex- tra information from the irregularly sampled and sparse, while simultaneously sidestepping the issue of jointly modeling both the ...
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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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Building Blocks for Variational Bayesian Learning of Latent Variable Models
... of latent variable models can be constructed from these blocks, including nonlinear and variance models, which are lacking from most existing variational ...various models is easy ...
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Learning latent variable models : efficient algorithms and applications
... interpretable models able to generate plausible data in challenging contexts exist, and have been object of an increasing interest in the deep learning community during the last few ...These models, called ...
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Online Tensor Methods for Learning Latent Variable Models
... This paper builds on the recent works of Anandkumar et al (Anandkumar et al., 2012, 2013b) which establishes the correctness of tensor-based approaches for learning MMSB (Airoldi et al., 2008) models and other ...
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Robustness in Latent Variable Models
... of latent variable models in the structural measurement error models and joint ...The models we studied cover a wide range of latent-variable models that are ...
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Latent Variable Models of Selectional Preference
... Latent variable models that use EM for infer- ence can be very sensitive to the number of latent variables chosen. For example, the performance of ROOTH-EM worsens quickly if the number of ...
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Bayesian latent variable models for collaborative item rating prediction
... in models such as Probabilistic Latent Semantic Indexing (pLSI) ...than models that assume each document is only drawn from a single ...to models based on LDA with both the added flexibility ...
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Perturbative Corrections for Approximate Inference in Gaussian Latent Variable Models
... In order to justify the usefulness of EP for Ising models we therefore need an alternative argu- ment. Our argument is entirely restricted to Gaussian EP for our extended definition of GLVMs and do not extend to ...
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Semi supervised latent variable models for sentence level sentiment analysis
... Both models leverage abundant natural super- vision in the form of review ratings, as well as a small amount of manually crafted sentence labels, to learn sentence-level sentiment clas- ...component models ...
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Gaussian Process Latent Variable Models for Visualisation of High Dimensional Data
... a latent space to the observed ...cess latent variable model (GPLVM) is then evaluated as an approach to the visualisation of high dimensional data for three different ...
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Spectral Methods for Learning Latent Variable Models: Unsupervised and Supervised Settings
... “A Tensor Spectral Approach to Learning Mixed Membership Community Models” by A.. Outline[r] ...
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Analysis of the readiness to buy cultural tourism online by means of latent variable models
... The growing importance that the Internet is beginning to acquire as a tool for distribution in the world of travel is conditioning the marketing and management strategies of tourism providers. These have an ever more ...
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