latent variable
Penalized Latent Variable Estimator For Finite Mixture of Regression Models.
... population, variable selection and estimation is a problem that has extensive research and ...penalized latent variable regression analysis ...
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Latent Variable Models for Semantic Orientations of Phrases
... Figures 2, 3, 4 show cross-validated accuracy values for various values of β, respectively yielded by the 3-PLSI model, the triangle model and the U-shaped model with different numbers M of pos- sible states for the ...
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Latent variable models for binary response data
... Tsutakawa (1984) derived a MML procedure employing the two-parameter logistic model. His method differs from Bock and Aitkin's method in the manner in which the prior distribution of the latent variable is ...
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Self Training with Products of Latent Variable Grammars
... The latent variable approach of Petrov et ...uses latent variables to refine the context-free assumptions encoded in the ...the latent variable gram- mars are not explicitly ...
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Latent Variable Synchronous CFGs for Hierarchical Translation
... The latent-variable model is inte- grated into the standard MT pipeline by comput- ing marginal probabilities for each rule in the parse forest of a source sentence using the algorithm in Figure 1 with the ...
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Latent Variable Model for Multi modal Translation
... a latent variable model. This latent variable can be seen as a multi-modal stochastic embedding of an image and its de- scription in a foreign ...our latent variable MMT ...
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Probabilistic Distributional Semantics with Latent Variable Models
... the latent variable value for each observation according to the conditional probability distribution determined by the current observed and latent variable values for all other ...
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A Latent Variable Model for Geographic Lexical Variation
... Supervised Latent Dirichlet Allocation In a more subtle version of the mixture-of-unigrams model, we model each author as an admixture of re- ...the latent variable attached to each au- thor is no ...
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Experiments with Spectral Learning of Latent Variable PCFGs
... Latent-variable PCFGS (L-PCFGs) are a highly suc- cessful model for natural language parsing (Mat- suzaki et al., 2005; Petrov et al., 2006). Recent work (Cohen et al., 2012) has introduced a spectral ...
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A dynamic latent variable model for source separation
... a latent variable model, called the dynamic DLVM, for modeling time varying non-negative ...dynamic latent variable models, our model does not require any free parameter (except the number of ...
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A Discriminative Latent Variable Model for Statistical Machine Translation
... Large-scale discriminative machine transla- tion promises to further the state-of-the-art, but has failed to deliver convincing gains over current heuristic frequency count systems. We argue that a principle reason for ...
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Building Blocks for Variational Bayesian Learning of Latent Variable Models
... valued latent variables offer an important advantage over point estimates, because they are robust against overfitting and provide a cost function suitable for learning model ...Moreover, latent ...
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Perturbative Corrections for Approximate Inference in Gaussian Latent Variable Models
... EP is introduced in a general way in Section 3, making it clear how various degrees of complexity can be included in its approximating structure. The partition function will be used throughout the paper to explain the ...
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Spectral Learning of Latent-Variable PCFGs: Algorithms and Sample Complexity
... We introduce a spectral learning algorithm for latent-variable PCFGs (Matsuzaki et al., 2005; Petrov et al., 2006). Under a separability (singular value) condition, we prove that the method provides ...
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Learning latent variable models : efficient algorithms and applications
... interpretable description of the data – via the structure of the LVM – and an effective generator for synthetic data. It is interesting to observe that not- interpretable models able to generate plausible data in ...
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A biological, latent variable model of health (EarlyBird 68)
... Two conclusions can be drawn from our data. First, our data provide a rationale for aggregating across several different kinds of biomarkers in biopsychological research. Second, existence of the latent ...
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A Provably Correct Learning Algorithm for Latent Variable PCFGs
... We introduce a provably correct learning algorithm for latent-variable PCFGs. The algorithm relies on two steps: first, the use of a matrix-decomposition algorithm ap- plied to a co-occurrence matrix ...
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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 features, ...
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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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Priors for Diversity in Generative Latent Variable Models
... on latent parameters with a determinantal point process ...possible latent distributions, via a positive definite ...generative latent variable model in which diverse sets of latent ...
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