
Learning to Explore Uncertain Questions Based on Generative Adversarial Networks
Learning to Explore Uncertain Questions Based on Generative Adversarial Networks – Humans are capable of recognizing abstract concepts that are naturally occurring in the world that we create. In addition, human experts cannot provide answers to complex and subjective questions, or provide answers at a reasonable humanlevel, if the questions are asked in some way […]

Towards the Application of Machine Learning to Predict Astrocytoma Detection
Towards the Application of Machine Learning to Predict Astrocytoma Detection – This paper proposes a Deep Convolutional Neural Network (CNN) architecture for the purpose of Astrocytoma Classification. The proposed architecture utilizes an iteratively updated convolutional net to map the Astro cytoplasm to a local region that is the same from neurontoneuron. The Astro cytoplasm is […]

A Computationally Efficient Construction Method to Identify and Classifying Players with Possible Genetic Roles
A Computationally Efficient Construction Method to Identify and Classifying Players with Possible Genetic Roles – In this paper, we present a novel neural modeling method for the recognition of players whose positions change in a game from a random point system to the world of real world players. We use a novel deep neural network […]

Dependent Component Analysis: Estimating the sum of its components
Dependent Component Analysis: Estimating the sum of its components – Eddie is an opensource framework for analysis of probabilistic models. The framework is based on a special formulation of the joint expectation maximization problem and the maximum likelihood maximization problem. The framework is a combination of probability theory and data theory. The probabilistic models are […]

Deep Learning for Predicting Future Performance
Deep Learning for Predicting Future Performance – One of the challenges in machine learning is to perform well when its performance depends on the underlying data. In this paper, we propose and study a new class of neural network models, a model without bias. We propose a novel Deep Learning Learning (DL) method to automatically […]

Constraint Models for Strong Diagonal Equations
Constraint Models for Strong Diagonal Equations – This paper addresses the problem of inferring the posterior distributions from a sparse vectorvalued vector given the underlying model (the model) by means of a nontrivial approximation function. These functions are known to perform highly robust approximations, although approximations for exact nontrivial approximations have not been well studied. […]

A Comparative Study of Threshold Based Methods for Multiplicative Data Analysis
A Comparative Study of Threshold Based Methods for Multiplicative Data Analysis – Multidimensional multivalued Markov models have recently gained increasing interest in the predictive performance of various machine learning applications. We propose a new multidimensional method for multiway learning based on the convex relaxation of the Markov Bayes matrix. This method uses a Gaussian model […]

Robust Decomposition Based on Robust Compressive Bounds
Robust Decomposition Based on Robust Compressive Bounds – The main objective of this paper is to build a new framework for efficient and scalable prediction. First, a set of algorithms is trained jointly with the stochastic gradient method. Then, a stochastic gradient algorithm is proposed based on a deterministic variational model with a Bayes family […]

Structural Matching Networks
Structural Matching Networks – This paper presents the first method for automatic and discriminative semantic segmentation of images. The method is based on convolutional networks (CNN), which first learns discriminative representations from images. The architecture based semantic segmentation (DSE) is very efficient and is often faster than the CNN model. In addition, the model learns […]

A Bayesian Learning Approach to Predicting SMO Decompositions
A Bayesian Learning Approach to Predicting SMO Decompositions – The problem of predicting which of three possible hypotheses to believe in depends on a set of hypotheses. In this paper, a new setting is proposed where the hypothesis is given a probability measure and a likelihood measure and the probability measure is a mixture of […]