Categorical variables are a natural choice for representing discrete structure in the world. However, stochastic neural networks rarely use categorical latent variables due to the inability to backpropagate through samples. In this work, we present an efficient gradient estimator that replaces the non-differentiable sample from a categorical distribution with a differentiable sample from a nove...

Categorical variables are a natural choice for representing discrete structure in the world. However, stochastic neural networks rarely use categorical latent variables due to the inability to backpropagate through samples. In this work, we present an efficient gradient estimator that replaces the non-differentiable sample from a categorical distribution with a differentiable sample from a nove...

In most application of the data classifications, the data sets contain both continuous and categorical variables. In other word, multivariate data sets containing mixtures of continuous and categorical variables arise frequently in practice. This paper presents a novel Probability Neural Network (PNN) which can classify the data for both continuous and categorical input data types. The case wit...

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It is worth noting that the variable-selection process has become an increasingly exciting challenge, given the dramatic increase in the size of databases and the number of variables to be explored and modelized. Therefore, several strategies and methods have been developed with the aim of selecting the minimum number of variables while preserving as much information for the interest variable o...

Data ......................................................................................................................483 Response and Explanatory Variables ..........................................................483 Weights ............................................................................................................484 Missing Data ...........................................

The problem of inference about the joint distribution of two categorical variables based on knowledge or observations of their marginal distributions, to be referred to as categorical data fusion in this paper, is relevant in statistical matching, ecological inference, market research, and several other related fields. This article organizes the use of proxy variables, to be distinguished from ...

Optimization of real-world black-box functions defined over purely categorical variables is an active area research. In particular, optimization and design biological sequences with specific functional or structural properties have a profound impact in medicine, materials science, biotechnology. Standalone search algorithms, such as simulated annealing (SA) Monte Carlo tree (MCTS), are typicall...