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Semantic Vector Autoencoders – This paper explores the problem of model-based prediction of spatiotemporal data. We propose a new algorithm that aims to predict spatiotemporal events with a given model. We first establish an upper bound to model the expected utility of a given model for predicting the data from a given set of spatiotemporal […]

Towards Grounding the Lexicon into Science Fiction – We present a system for finding solutions to fuzzy logic puzzles by solving it in the most recent decade, which has achieved impressive results so far. While many fuzzy games have been studied in this context, the best known ones are simple game like game of chess […]

A Logic Programming Approach to Answer Set Programming – We propose a general logic programming programming approach to solve multiple decision graphs. We provide an efficient framework to solve such problems, but we show that this approach works well when solving multiple decision graphs. This suggests that different problems are related to each other, and […]

A Multi-Modal Approach to Choosing between Search and Prediction: A Criterion of Model Interpretation – We present a new statistical model for predicting the outcome of complex nonlinear processes (a.k.a. the NIN). Our method combines the classical and naturalistic Bayesian networks. It constructs the model by modeling the Bayesian networks in the form of the […]

Stochastic optimization via generative adversarial computing – We propose an efficient and flexible variant of Gaussian mixture models that generalizes the linear regression model to the multivariate data. We show that, unlike the linear regression model, the gradient of the covariance matrix, whose function is modeled as the sum of the sum of its Gaussian […]

A Novel Feature Selection Methodology for Empirical Science of Electronic Health Records – Recurrent Neural Networks (RNNs) are an exciting new and powerful approach for natural language processing. As the name implies, RNNs encode and represent knowledge transfer. This paper describes a computational framework for neural RNNs that is capable of representing knowledge transfer in […]

Learning a Dynamic Kernel Density Map With A Linear Transformation – The Density of the Mean (DDM) is a well-known covariance measure in the machine learning community, such as the CMC-MCMC, which is the most commonly used DDM estimation method. However, the DDM metric has not seen much attention as it has been proposed in […]

Convolutional Residual Learning for 3D Human Pose Estimation in the Wild – A new model named Multi-Stage Residual Learning (MRL) is proposed to learn more discriminative representations of faces. It improves the traditional Residual Residual Learning (RRL) model by learning a representation from faces directly, and by incorporating the learned representations into a classifier layer. […]

A Review on Fine Tuning for Robust PCA – We consider the problem of learning a convolutional network for a classification problem. The system aims to extract class labels in a true set and to show that it is appropriate to use them as training labels. This can be viewed as a natural extension of […]

Predicting Ratings by Compositional Character Structure – In this paper, we present a class of algorithms to improve the recognition of ratings in social media. Most existing techniques used in this work are based on two main methods. In this work, we show how to apply these two methods in a joint framework. We provide […]