Scalable Large-Scale Image Recognition via Randomized Discriminative Latent Factor Model – In this article, we propose a new recurrent neural network architecture for the semantic segmentation task. The proposed architecture is a fully convolutional network for semantic segmentation. This architecture is trained from scratch using Convolutional Neural Networks (CNNs). The performance of the recurrent network of the proposed architecture is evaluated using PASCAL VOC 2015 and the results show that the proposed architecture reduces the visual segmentation time by 50% with no loss in segmentation speed and by a 20% loss in accuracy compared to the traditional Convolutional Neural Network-based solutions. Overall, the proposed architecture yields about 30% improvement over the state-of-the-art results in terms of segmentation speed compared to the state-of-the-art CNN models.

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.

Tightly constrained BCD distribution for data assimilation

# Scalable Large-Scale Image Recognition via Randomized Discriminative Latent Factor Model

Towards Large-Margin Cost-Sensitive Deep Learning

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.

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