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[PDF] Top 20 Robustness in Latent Variable Models

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Robustness in Latent Variable Models

Robustness in Latent Variable Models

... Statistical models involving latent variables are widely used in many areas of applications, such as biomedical science and social ...the latent variables are often invoked. As latent ... See full document

90

Learning the Structure of Linear Latent Variable Models

Learning the Structure of Linear Latent Variable Models

... the robustness of the algorithm, since some edges of G are likely to be erroneously removed in small sample studies, although more elaborated ways of adding edges back would need to be discussed in detail and are ... See full document

56

Particle methods for maximum likelihood estimation in latent variable models

Particle methods for maximum likelihood estimation in latent variable models

... obvious technique is monitoring the marginal posterior of every parameter combi- nation which is sampled and using that set of parameters associated with the largest value seen. The only obvious advantage of this method ... See full document

16

Bayesian latent variable models for collaborative item rating prediction

Bayesian latent variable models for collaborative item rating prediction

... successful models compet- ing in the Netflix prize also estimate a bias for each user and a bias for each item as well as the bias due to the user and the item ... See full document

10

Penalized Latent Variable Estimator For Finite Mixture of Regression Models.

Penalized Latent Variable Estimator For Finite Mixture of Regression Models.

... Clusterwise linear regression techniques can be categorized by two main approaches, algorithmic and likelihood approaches. For context, the term algorithmic implies a variation of a semi-exhaustive combinatorial method. ... See full document

73

Online Tensor Methods for Learning Latent Variable Models

Online Tensor Methods for Learning Latent Variable Models

... two latent variable mod- eling problems namely, (1) community detection, in which we learn the latent communities that the social actors in social networks belong to, and (2) topic modeling, in which ... See full document

39

Language as a Latent Variable: Discrete Generative Models for Sentence Compression

Language as a Latent Variable: Discrete Generative Models for Sentence Compression

... proposed variable-sized variational auto-encoders to identify multiple objects in ...generative models for supervised learning ...generative models can be a statistically efficient method for ... See full document

10

Latent Variable Generative Models for Data Efficient Text Classification

Latent Variable Generative Models for Data Efficient Text Classification

... For example, value 1 is mostly associated with future and progressive tenses; the words “will”, “next”, and “new” appear frequently. Value 2 tends to contain past and perfect verb tenses (the phrases “has been” and “have ... See full document

11

Perturbative Corrections for Approximate Inference in Gaussian Latent Variable Models

Perturbative Corrections for Approximate Inference in Gaussian Latent Variable Models

... Expectation Propagation (EP) provides a framework for approximate inference. When the model under consideration is over a latent Gaussian field, with the approximation being Gaussian, we show how these ... See full document

42

Semi supervised latent variable models for sentence level sentiment analysis

Semi supervised latent variable models for sentence level sentiment analysis

... In this paper, we demonstrate how combining coarse-grained and fine-grained supervision bene- fits sentence-level sentiment analysis – an important task in the field of opinion classification and retrieval (Pang and Lee, ... See full document

6

Overcoming the Memory Bottleneck in Distributed Training of Latent Variable Models of Text

Overcoming the Memory Bottleneck in Distributed Training of Latent Variable Models of Text

... While the general minimization problem we en- counter is NP-hard, we develop greedy approxima- tions that work well. In experiments with both HMM and LDA models, we show that our technique offers large advantages ... See full document

6

A Latent Variable Recurrent Neural Network for Discourse Driven Language Models

A Latent Variable Recurrent Neural Network for Discourse Driven Language Models

... graphical models that were previously the dominant machine learning approach for NLP, neural archi- tectures lack ...cal models can marginalize over annotations that are unavailable at test or training ... See full document

11

Open Domain Event Extraction Using Neural Latent Variable Models

Open Domain Event Extraction Using Neural Latent Variable Models

... We thank the anonymous reviewers for their valu- able comments and suggestions. We thank Kiem- Hieu Nguyen from Hanoi University of Science and Technology for providing source code and solving confusions for their work. ... See full document

12

Child health and epidemiology with Bayesian geo additive latent variable models

Child health and epidemiology with Bayesian geo additive latent variable models

... indicators for the latent variables morbidity and malnutrition. This allows studying the common impact of risk factors and geographical location on these latent variables, thereby taking account of ... See full document

29

Infant Neurobehavioral Dysregulation: Behavior Problems in Children With Prenatal Substance Exposure

Infant Neurobehavioral Dysregulation: Behavior Problems in Children With Prenatal Substance Exposure

... First, latent variables, which are unob- served constructs that represent sta- tistically related observed variables, were developed to measure prenatal substance exposure, neurobehavioral dysregulation on the ... See full document

10

Building Blocks for Variational Bayesian Learning of Latent Variable Models

Building Blocks for Variational Bayesian Learning of Latent Variable Models

... The basic algorithm for learning and inference is based on updating a variable at a time while keeping other variables fixed. It has the benefits of being completely local and guaranteed to con- verge. A drawback ... See full document

47

Implicit Deep Latent Variable Models for Text Generation

Implicit Deep Latent Variable Models for Text Generation

... high-quality latent features, and learning a better ...language models derived from the PTB training corpus; (2) Reverse PPL: the fluency of PTB corpus based on language model derived from the generated ... See full document

11

A Comparison of Latent Variable Models For Conversation Analysis

A Comparison of Latent Variable Models For Conversation Analysis

... In order to do this, we model each utterance in a conversation as dependent on the speaker, the ad- dressee and the other people present. As we shall describe, our models encode the belief that people speak/behave ... See full document

9

Latent variable models for binary response data

Latent variable models for binary response data

... Bock and Aitkin (1981) have considered besides a prior standard normal distribution, a rectangular and an empirical distribution for the single latent variable and taken k-10 (see their paper for more ... See full document

356

Latent variable models for mixed manifest variables

Latent variable models for mixed manifest variables

... LATHAT VARIABLN MODELS FOR MIXED MANIFEST VARIABLES Irini Moustaki London School of Economics and Political Science University of London Submitted in Fulfilment of the Requirement for the Degree of Do[.] ... See full document

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