Weights for the Quality Latent Variable
Robustness in Latent Variable Models
... assumed latent-variable model being the true model, the low rejection rates resulting from mixture-normal modeling suggest that the testing procedure with test statistics T 1,1 ∗ and T 1,2 ∗ has a ...
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Latent Variable Sentiment Grammar
... associates each sentiment signal with a continuous vector instead of a discrete variable. Experiments on SST show that explicit mod- eling of sentiment composition leads to signifi- cantly improved performance ...
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Latent Variable Realism in Psychometrics
... accept TA-2 according to which a test is valid for measuring an attribute A iff the attribute exists and variations in A causally produce variations in measurement outcomes, then the claim that reliability is necessary ...
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Portfolio Selection under Condition of Variable Weights
... = 1, , i n 3. Existence of Riskless Securities Note that a security is riskless, if its return is guaranteed and hence it is deterministic. Although cash accounts are commonly considered as riskless securities, in our ...
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Spectral Learning of Latent Variable PCFGs
... • For each a ∈ N , σ a is the value of the m’th largest singular value of Ω a . Define σ = min a σ a . We then have the following theorem: Theorem 3 Assume that the inputs to the algorithm in figure 4 are i.i.d. draws ...
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Latent Variable PCFGs: Background and Applications
... rely heavily on linguistic knowledge of English, and as such they do not generalize to treebanks in other languages. With all of this previous work, nonterminal re- finement is central to the underlying parsing for- ...
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Latent Variable Dialogue Models and their Diversity
... In this paper, we present a latent variable model for one-shot dialogue response, and investigate what kinds of diversity the latent variables capture. Our experiments show that our model has higher ...
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Parsing German with Latent Variable Grammars
... To prevent oversplitting, we could measure the utility of splitting each latent annotation individu- ally and then split the best ones first. However, not only is this impractical, requiring an entire training ...
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Latent class analysis variable selection
... search variable selection and clus- tering algorithm for the case of discrete data modeled by conditionally independent multi- nomially distributed ...each latent class model fitted in this algorithm one ...
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Latent Variable Models of Selectional Preference
... to surface-level predicates such as verbs and mod- ifiers, but also extends to semantic frames (Erk, 2007) and inference rules (Pantel et al., 2007). The fundamental problem that selectional prefer- ence models must ...
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Products of Random Latent Variable Grammars
... Abstract We show that the automatically induced latent variable grammars of Petrov et al. (2006) vary widely in their underlying representations, de- pending on their EM initialization point. We use this to ...
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LATENT VARIABLE SEQUENTIAL SET TRANSFORMERS
... D.5 O MNIGLOT A DDITIONAL D ETAILS AND R ESULTS The LSTM baseline used in our Omniglot experiments have a hidden size of 128 and use c = 4 latent modes. The input is first projected into this space using an ...
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Latent-Variable PCFGs: Background and Applications
... rely heavily on linguistic knowledge of English, and as such they do not generalize to treebanks in other languages. With all of this previous work, nonterminal re- finement is central to the underlying parsing for- ...
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Approximation methods for latent variable models
... In chapter 4, the results of the experiments showed the expediency of using fac- torised Gaussians as variational approximations. The VEM estimators themselves were not shown to provide a positive trade-off between ...
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Advances in Latent Variable and Causal Models
... The latent variable model in this setting should thus be thought of as a true descriptive model for the world, in contrast to the deep generative modelling setting, where latent variable ...
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Three Contributions to Latent Variable Modeling
... Templin and Bradshaw (2014) proposed a hierarchical diagnostic classification model (HDCM) and analyzed the Examination for the Certificate of Proficiency in English (ECPE) dataset. There are 28 items measuring 3 ...
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Linking response quality to survey engagement: a combined random scale and latent variable approach
... tent variable which is used to model the values of a number of indicators of survey engagement in a measurement model component, as well as explaining scale hetero- geneity within the choice ...
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Latent-Variable Modeling: Algorithms, Inference, and Applications
... These latent (hidden) variables complicate finding a concise representation, as they introduce confounding depen- dencies among the variables of ...of latent phenomena in statistical modeling via ...
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Probabilistic Distributional Semantics with Latent Variable Models
... 3.4.3 Choosing | Z | . In the “parametric” latent variable models used here the number of topics or semantic classes, | Z | , must be fixed in advance. This brings significant efficiency advantages but also ...
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Nonconvex Global Optimization for Latent Variable Models
... the latent variables, one must be able to project it to a nearby feasible one, by projecting either the fractional parameters or the fractional latent variables into the feasible space and then solving ...
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