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Sparse Deep Structured Prediction with Latent Variables – Generative adversarial networks (GANs) have been widely used for probabilistic inference, but many problems involved in GANs are computationally intractable. This paper presents a novel approach that solves this problem by training a recurrent neural network to predict the target target from an input distribution vector. The […]

Deep End-to-End Neural Stacking – In this paper, we describe a deep learning (DL) framework for segmentation of the human hippocampus. The hippocampus is considered as a functional brain region that contains various sensory and motor functions. In this context, a neural network (NN) has received attention in recent years. However, the classification of the […]

Efficient Estimation of Local Feature Distribution – We propose a new formulation of the gradient descent problem that uses a mixture of Gaussian and the sum of a combination of Dirichlet processes. We have a new perspective on the problem of estimating the gradient of multiple Gaussian processes by considering the maximum and minimum distance […]

Coupled Itemset Mining with Mixture of Clusters – This paper proposes a method for generating reusable, scalable, high-quality, distributed multi-domain image datasets. We propose a new approach that consists of two parts. The first part is to partition the domain into clusters to reduce the number of redundant features. The second part is to construct […]

Deep Learning with Global Model Aggregation – We address the problem of learning and classifying a multi-model classification problem without relying on a human visualizer. In this paper, we study multiple-model classification in a video of a teacher performing a teacher-student collaborative task: to model student behaviour in an online setting, we have a novel, […]

Identifying relevant variables via probabilistic regression models – We propose a new approach for learning a neural network from random images by using a nonlinear function as a surrogate for a feature set. By modeling the nonlinear function, we leverage its nonlinearity in learning (uniformity between distributions for which a model is expected to predict). […]

Estimating Processes based on Random Walks Interval and Frequency from Time-Lapse Data – A number of approaches have been proposed for the task of automated time series segmentation. In this paper, we propose an automatic segmentation method using two data-driven algorithms. First, we design a random walk algorithm, which can extract segments with the best […]

A Semantic Matching Based Algorithm for Multi-Party Conversations: Application to House Orienteering – We propose a novel algorithm for constructing intelligent systems that interactively interact and produce intelligent systems that interactively interact with their environment: natural language dialogues. The system is a natural language object from the viewpoint of a natural conversation, which is the […]

A novel algorithm for learning binary classification problems from patient-based data – This article addresses the problem of predicting accurate and timely diagnosis of a disease-causing cause of an individual. The goal is to collect a dataset with an average of 100,000 patients each day, and to assess the predictability of such a dataset using […]

On the Relation between Human Image and Face Recognition – We present a new method for extracting human faces from facial data of different human facial expressions. Our method is based on convolutional neural networks, which consists of recurrent layers to encode the human face state, then the convolution layers to learn the discriminative feature […]