Latent variable model ≈ Latent variable model
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Density Estimation Using Real NVP Open
Unsupervised learning of probabilistic models is a central yet challenging problem in machine learning. Specifically, designing models with tractable learning, sampling, inference and evaluation is crucial in solving this task. We extend t…
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The unity and diversity of executive functions: A systematic review and re-analysis of latent variable studies. Open
Confirmatory factor analysis (CFA) has been frequently applied to executive function measurement since first used to identify a three-factor model of inhibition, updating, and shifting; however, subsequent CFAs have supported inconsistent …
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Evaluation of Variance Inflation Factors in Regression Models Using Latent Variable Modeling Methods Open
A procedure that can be used to evaluate the variance inflation factors and tolerance indices in linear regression models is discussed. The method permits both point and interval estimation of these factors and indices associated with expl…
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Intent Contrastive Learning for Sequential Recommendation Open
Users' interactions with items are driven by various intents (e.g., preparing\nfor holiday gifts, shopping for fishing equipment, etc.).However, users'\nunderlying intents are often unobserved/latent, making it challenging to\nleverage suc…
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Testing measurement invariance in longitudinal data with ordered-categorical measures. Open
A goal of developmental research is to examine individual changes in constructs over time. The accuracy of the models answering such research questions hinges on the assumption of longitudinal measurement invariance: The repeatedly measure…
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A more general model for testing measurement invariance and differential item functioning. Open
The evaluation of measurement invariance is an important step in establishing the validity and comparability of measurements across individuals. Most commonly, measurement invariance has been examined using 1 of 2 primary latent variable m…
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Psychometric Network Models from Time-Series and Panel Data Open
Researchers in the field of network psychometrics often focus on the estimation of Gaussian graphical models (GGMs)—an undirected network model of partial correlations—between observed variables of cross-sectional data or single-subject ti…
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A Latent Variable Model Approach to PMI-based Word Embeddings Open
Semantic word embeddings represent the meaning of a word via a vector, and are created by diverse methods. Many use nonlinear operations on co-occurrence statistics, and have hand-tuned hyperparameters and reweighting methods. This paper p…
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An Introduction to Network Psychometrics: Relating Ising Network Models to Item Response Theory Models Open
In recent years, network models have been proposed as an alternative representation of psychometric constructs such as depression. In such models, the covariance between observables (e.g., symptoms like depressed mood, feelings of worthles…
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A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues Open
Sequential data often possesses hierarchical structures with complex dependencies between sub-sequences, such as found between the utterances in a dialogue. To model these dependencies in a generative framework, we propose a neural network…
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Full Information Maximum Likelihood Estimation for Latent Variable Interactions With Incomplete Indicators Open
Researchers have developed missing data handling techniques for estimating interaction effects in multiple regression. Extending to latent variable interactions, we investigated full information maximum likelihood (FIML) estimation to hand…
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PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable Open
Pre-training models have been proved effective for a wide range of natural language processing tasks. Inspired by this, we propose a novel dialogue generation pre-training framework to support various kinds of conversations, including chit…
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gllvm: Fast analysis of multivariate abundance data with generalized linear latent variable models in <span>r</span> Open
There has been rapid development in tools for multivariate analysis based on fully specified statistical models or ‘joint models’. One approach attracting a lot of attention is generalized linear latent variable models (GLLVMs). However, s…
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Language as a Latent Variable: Discrete Generative Models for Sentence Compression Open
In this work we explore deep generative models of text in which the latent representation of a document is itself drawn from a discrete language model distribution.We formulate a variational auto-encoder for inference in this model and app…
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Neural Latent Extractive Document Summarization Open
Extractive summarization models need sentence level labels, which are usually created with rule-based methods since most summarization datasets only have document summary pairs. These labels might be suboptimal. We propose a latent variabl…
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Latent Variable Models and Networks: Statistical Equivalence and Testability Open
Networks are gaining popularity as an alternative to latent variable models for representing psychological constructs. Whereas latent variable approaches introduce unobserved common causes to explain the relations among observed variables,…
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Spherical Latent Spaces for Stable Variational Autoencoders Open
A hallmark of variational autoencoders (VAEs) for text processing is their combination of powerful encoder-decoder models, such as LSTMs, with simple latent distributions, typically multivariate Gaussians. These models pose a difficult opt…
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Kinds<i>versus</i>continua: a review of psychometric approaches to uncover the structure of psychiatric constructs Open
The question of whether psychopathology constructs are discrete kinds or continuous dimensions represents an important issue in clinical psychology and psychiatry. The present paper reviews psychometric modelling approaches that can be use…
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Thinking thrice about sum scores, and then some more about measurement and analysis Open
Measurement is fundamental to all research in psychology and should be accorded greater scrutiny than typically occurs. Among other claims, McNeish and Wolf (Thinking twice about sum scores. Behavior Research Methods , 52 , 2287-2305) argu…
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Cross-Lagged Network Models Open
Network theory and accompanying methodology are becoming increasingly popular as an alternative to latent variable models for representing and, ultimately, understanding psychological constructs. The core feature of network models is that …
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Relating latent class membership to external variables: An overview Open
In this article we provide an overview of existing approaches for relating latent class membership to external variables of interest. We extend on the work of Nylund‐Gibson et al. ( Structural Equation Modeling: A Multidisciplinary Journal…
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A review of dynamic network models with latent variables Open
We present a selective review of statistical modeling of dynamic networks. We focus on models with latent variables, specifically, the latent space models and the latent class models (or stochastic blockmodels), which investigate both the …
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Measurement Invariance and Differential Item Functioning in Latent Class Analysis With Stepwise Multiple Indicator Multiple Cause Modeling Open
The use of latent class analysis, and finite mixture modeling more generally, has become almost commonplace in social and health science domains. Typically, research aims in mixture model applications include investigating predictors and d…
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Rethinking Action Spaces for Reinforcement Learning in End-to-end Dialog Agents with Latent Variable Models Open
Tiancheng Zhao, Kaige Xie, Maxine Eskenazi. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
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Lagging Inference Networks and Posterior Collapse in Variational Autoencoders Open
The variational autoencoder (VAE) is a popular combination of deep latent variable model and accompanying variational learning technique. By using a neural inference network to approximate the model's posterior on latent variables, VAEs ef…
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Two-Step Estimation of Models Between Latent Classes and External Variables Open
We consider models which combine latent class measurement models for categorical latent variables with structural regression models for the relationships between the latent classes and observed explanatory and response variables. We propos…
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Learning Disentangled Semantic Representation for Domain Adaptation Open
Domain adaptation is an important but challenging task. Most of the existing domain adaptation methods struggle to extract the domain-invariant representation on the feature space with entangling domain information and semantic information…
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Towards Conceptual Compression Open
We introduce a simple recurrent variational auto-encoder architecture that significantly improves image modeling. The system represents the state-of-the-art in latent variable models for both the ImageNet and Omniglot datasets. We show tha…
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Meta Reinforcement Learning with Latent Variable Gaussian Processes Open
Learning from small data sets is critical in many practical applications where data collection is time consuming or expensive, e.g., robotics, animal experiments or drug design. Meta learning is one way to increase the data efficiency of l…
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Bayesian Comparison of Latent Variable Models: Conditional Versus Marginal Likelihoods Open
Typical Bayesian methods for models with latent variables (or random effects) involve directly sampling the latent variables along with the model parameters. In high-level software code for model definitions (using, e.g., BUGS, JAGS, Stan)…