Stacked Extraction and Characterization of Object Categories from Camera Residuals – This paper proposes an image recognition method that employs a hierarchical representation for categorization and segmentation in order to reduce the number of features for training and segmentation. We proposed a fully Convolutional neural network with a stacked architecture built specifically for classification and segmentation. The structure of this new architecture is described in terms of an evolutionary algorithm with no explicit feature selection or feature tracking. To validate the performance of the proposed method, a detailed analysis of the hierarchical representation with features from different cameras is presented. The proposed architecture can be viewed as an extension of the convolutional neural network architecture, as we show that it does not have any loss or loss with respect to a deep network. Using the new architecture our method performs a high accuracy classification task in ImageNet (7.2%) while performing at least 20.8% accuracy on the classification task as compared to the baseline.

This paper explores the notion of a data manifold that is composed of two discrete sets of variables. By means of a multivariate Bayesian system model, a model that allows estimation of the manifold, the manifold is then fed to various probabilistic models, where the parameters of each model are learned in this manifold, and then the data manifold is further used for inference. The inference process is defined as a learning of probability distributions over discrete models. In this paper, we provide an algorithmic framework for training Bayes’ models on manifolds, where the manifold is learned using the multivariate Bayesian system model. The system model allows for both the ability of the inference process to be expressed as a data matrix, and the data manifold can be represented as a discrete set of Bayesian data as used for estimation and inference. The approach can be interpreted as a multivariate probabilistic system and the inference process is defined as a Bayesian inference of probability distributions over discrete models with the multivariate system model.

Learning a Universal Representation of Objects

Neural Networks for Activity Recognition in Mobile Social Media

# Stacked Extraction and Characterization of Object Categories from Camera Residuals

Fast Relevance Vector Analysis (RVTA) for Text and Intelligent Visibility Tasks

Exploiting Sparse Data Matching with the Log-linear Cost Function: A Neural Network PerspectiveThis paper explores the notion of a data manifold that is composed of two discrete sets of variables. By means of a multivariate Bayesian system model, a model that allows estimation of the manifold, the manifold is then fed to various probabilistic models, where the parameters of each model are learned in this manifold, and then the data manifold is further used for inference. The inference process is defined as a learning of probability distributions over discrete models. In this paper, we provide an algorithmic framework for training Bayes’ models on manifolds, where the manifold is learned using the multivariate Bayesian system model. The system model allows for both the ability of the inference process to be expressed as a data matrix, and the data manifold can be represented as a discrete set of Bayesian data as used for estimation and inference. The approach can be interpreted as a multivariate probabilistic system and the inference process is defined as a Bayesian inference of probability distributions over discrete models with the multivariate system model.