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Learning with the RNNSND Iterative Deep Neural Network – Neural networks (NNs) have been used for many tasks such as object recognition and pose estimation. In this paper we first show that neural networks can be used for non-linear classification without using any hand-crafted features and with a deep set of labeled data. The dataset […]

Deep Residual Learning for Automatic Segmentation of the Left Ventricle of Cardiac MRI – Recently, automatic segmentation is a key issue of biostemological imaging tasks. Although this is a challenging task, it is also an important one. In this research, we first propose an automatic segmentation method that combines the multi- and multi-dimensional data. For […]

Multilabel Classification using K-shot Digestion – A non-parametric model is computed within a learning-based framework based on the Bayesian nonparametric algorithm. This is based on an efficient search tree model based on an efficient multilabel clustering algorithm. The approach is developed using the model’s nonparametric feature set to obtain non-parametric features that are used to […]

Learning Graphical Models of Text to Artifacts – In this work we investigate the problem of using a semantic graph model to represent texts. We first present a graph model that learns to extract semantic relationships given their data. Our approach is based on using a text graph to describe each line of text. Our […]

Learning to Generate Chairs with Pointwise Loss Functions – In this work we develop a generic approach based on the Bayesian clustering algorithm. Our clustering algorithm combines two related objectives: clustering between pairs of random variables and clustering between clusters of points. The main contribution of our method is the use of the similarity between […]

Predictive Nonlinearity in Linear-Quadratic Control Problems – This paper presents a method for analyzing high-dimensional nonlinear regression problems through a probabilistic method of integrating covariates that does not depend on any covariates by using the statistical distributions of covariates of the underlying nonlinear mixture. The key idea is to model, in the form of a […]

A Multi-Class Online Learning Task for Learning to Rank without Synchronization – The problem of learning a Markov Decision Process (MDP) framework from scratch has been attracting a lot of interest over the last few years. However, the problem in many of its applications is still extremely challenging and the exact solution is still in […]

An Empirical Study of Neural Relation Graph Construction for Text Detection – Conceptual logic provides a mechanism for reasoning about logic-like representations of language that can be used in a variety of applications, including data mining, human-computer interface and machine translation. Given basic logic, it can be easily inferred from the language, as we will […]

Learning Nonlinear Embeddings from Large and Small Scale Data: An Overview – In this paper, we take a detailed look at the problem of solving linear optimization problems that require only the problem-specific parameters or no constraints. Our goal is to find a suitable algorithm for each of the above-described data sets, by using the […]

A new Stochastic Unsupervised Approach to Patient-Specific Heartbeat Prediction – Deep learning has been widely used to discover, understand and manage complex patterns in data. While recent experiments on deep learning systems based on deep neural networks have shown great success in learning and predicting heart beats, the underlying machine learning paradigm of learning from […]