Learning with Variational Inference and Stochastic Gradient MCMC


Learning with Variational Inference and Stochastic Gradient MCMC – Most machine learning algorithms assume training data are spatially independent given the training samples and the samples are spatially independent. We show that a natural way to train a statistical machine is to extract a model from data and show how to find the most suitable candidate model for this setting. This is a challenging task since the problem we are proposing is that learning the latent representation of observed data can be done by exploiting the regularization problem. In this paper, we propose to learn the model via a regularizer which allows us to learn the latent representation. We compare different regularizers on the problem in detail and propose three algorithms to learn the latent representation and the model. We also show how to apply the two regularizers to the task of learning the model. Experiments on real world datasets show that the regularizers can substantially improve performance on the task of learning the latent representation and the model. A new dataset of users using a novel type of social system called Social Network is made available to demonstrate the proposed technique.

This paper presents a novel learning-based framework to identify the causal structure (i.e., the influence of several factors, like social, cultural and technical) in an individual’s performance. We propose a novel algorithm to recover the causal relation from data captured from different domains: a product of one domain, another product from another domain, and so on. Experiments using a public dataset of US adults show that, in comparison to other methods, our proposed framework outperforms state-of-the-art methods on a variety of benchmarks.

Bayesian Models for Topic Models

An extended Stochastic Block model for learning Bayesian networks from incomplete data

Learning with Variational Inference and Stochastic Gradient MCMC

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  • The Data Science Approach to Empirical Risk Minimization

    Multi-modal Multi-domain Attention for Automatic Quality Assessment of Health ProductsThis paper presents a novel learning-based framework to identify the causal structure (i.e., the influence of several factors, like social, cultural and technical) in an individual’s performance. We propose a novel algorithm to recover the causal relation from data captured from different domains: a product of one domain, another product from another domain, and so on. Experiments using a public dataset of US adults show that, in comparison to other methods, our proposed framework outperforms state-of-the-art methods on a variety of benchmarks.


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