Category: Uncategorized

  • The Spatial Aspect: A Scalable Embedding Model for Semantic Segmentation

    The Spatial Aspect: A Scalable Embedding Model for Semantic Segmentation – We present a general framework for building an algorithm for the task of segmentation from a small number of images in which the segmentation is accomplished in two steps. Each image is represented by a rectangle representing shape of the shape, along its boundaries. […]

  • Learning Local Feature Embedding for Visual Tracking with Pairwise Sparse Regression

    Learning Local Feature Embedding for Visual Tracking with Pairwise Sparse Regression – We present a deep learning based method for the visual search task. The method is based on a deep learning framework to extract a subset of images from a set of images where the content of the image is strongly restricted. We focus […]

  • Bayesian nonparametric regression with conditional probability priors

    Bayesian nonparametric regression with conditional probability priors – We present a method to estimate the Bayesian posterior by combining two sets of samples from a posterior distribution with a priori posterior information. Specifically, we first combine the posterior distributions obtained by a priori posterior distribution with the probability of each sample being the same number […]

  • Improving MT Transcription by reducing the need for prior knowledge

    Improving MT Transcription by reducing the need for prior knowledge – This paper summarizes information generated by automated systems learning from their results. This is also a critical question for the system design community. A typical automated system, given to it the task of predicting a target model, takes three steps: (1) To create the […]

  • The Evolution of the Human Linguistic Classification Model

    The Evolution of the Human Linguistic Classification Model – The aim of this paper is to analyze the evolution of the human social networks. In particular, we propose to explore a model that was developed in 2003 to model the emergence of social networks and their evolution through time. We present a deep learning framework […]

  • Deep Spatial Representation and Semantic Analysis

    Deep Spatial Representation and Semantic Analysis – We propose a new method for learning semantic semantic models with the goal of providing an efficient method for using semantic knowledge from a single image as input. The proposed semantic-based model is composed of two tasks: semantic segmentation and semantic classification. In the semantic segmentation task, we […]

  • Improving the accuracy and comparability of classification models via LASSO

    Improving the accuracy and comparability of classification models via LASSO – We propose a novel approach for learning a model for a dynamic event based on a Bayesian network. The network is composed of a temporal component, a high-dimensional feature vector and a random vector. Our approach is inspired by the recurrent reinforcement learning paradigm. […]

  • Dopamine modulation of modulated adulthood extension

    Dopamine modulation of modulated adulthood extension – This paper presents a theoretical approach to identify a possible biological mechanism that plays a crucial role in neurocognitive processes. The hypothesis is that a neural coding system can facilitate the exploration of neural codes and, consequently, facilitate the exploration of the brain, a process that is driven […]

  • Predictive Nonlinearity in Linear-Quadratic Control Problems

    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 […]

  • Feature Selection with Generative Adversarial Networks Improves Neural Machine Translation

    Feature Selection with Generative Adversarial Networks Improves Neural Machine Translation – A recently proposed method for unsupervised translation (OSMT) is based on the idea of learning a deep neural network to translate objects by identifying the regions in which they should be localized. The OSMT algorithm learns the region that best localizes the object and […]