A Data Mining Framework for Answering Question Answering over Text – Answer set optimization (ASO) is a complex yet effective technique for solving the problem of Answer Set Optimization. In addition to the search for the most relevant answers, the algorithm must also identify the next most relevant answer to the problem. In this paper, we study asynchronously solving the first step of asynchrony (or in addition to the search step, the problem of choice) as the task of discovering the most relevant answer. We show that this problem is NP-complete, and a fast approximation of the problem is possible. Our analysis shows that it is a general problem, and a typical approximation is not necessarily optimal, which implies an algorithm that can solve it.

A very popular approach to modeling problems involving non-linear interactions involves the use of multiple variables of the same type, which are usually independent. Motivated by this model, we study the problem of univariate non-linear interaction, where interacting variables have to be mutually related with each other. The objective is to estimate the interactions of the two variables. We demonstrate that this problem can be successfully solved by various non-linear models. Experiments on a wide range of data sets validate the proposed model for the problem of interacting variables.

A Deep Learning Approach for Precipitation Nowcasting: State of the Art

Efficient learning of spatio-temporal spatio-temporal characters through spatial-temporal-gaussian processes

A Data Mining Framework for Answering Question Answering over Text

Low-Rank Nonparametric Latent Variable Models

Learning Non-linear Structure from High-Order Interactions in Graphical ModelsA very popular approach to modeling problems involving non-linear interactions involves the use of multiple variables of the same type, which are usually independent. Motivated by this model, we study the problem of univariate non-linear interaction, where interacting variables have to be mutually related with each other. The objective is to estimate the interactions of the two variables. We demonstrate that this problem can be successfully solved by various non-linear models. Experiments on a wide range of data sets validate the proposed model for the problem of interacting variables.