Fast Spatial-Aware Image Interpretation – In previous approaches, image-guided semantic segmentation (e.g., FERET and RASCAL-2011) relies on the prior knowledge of the image. We propose a novel approach to extract relevant semantic information from the image. The objective function is to learn the information from the semantic domain, in terms of a latent variable, and to estimate the posterior of the semantic part of the image and present such posterior with high accuracy. We propose the idea of nonlinear estimation, in which the semantic part of each image is estimated from the input image into a latent space, where the probability of obtaining the data is proportional to the posterior. We validate the idea on various benchmarks, including MNIST, CIFAR-10, ImageNet, and SVHN, and show that our proposed method outperforms the rest of the proposed algorithms.

Learning a large class of estimators (e.g., Gaussian process models) is a challenging problem. For the past decade, there has been much interest in generating estimators that achieve consistent improvement. In this work, we consider the problem of learning an estimator for a large class of estimators. In this paper we propose a novel estimator for several large class of estimators including Markov chains and conditional random fields. We use a modified version of the Residual Recurrent Neural Network (RRCNN) model, which is able to learn a conditional probability density estimator from data, without relying on the input of any estimator. Our model achieves state-of-the-art performance and is able to achieve better performance with less computation with the same model complexity. We apply our algorithm to a variety of large data sets generated by Bayesian networks and to a large-scale model classification problem.

Learning an RGBD Model of a Moving Object using Deep Learning

Genetic-Algorithms for Sequential Optimization of Log Radial Basis Function and Kernel Ridge Quasi-Newton Method

Fast Spatial-Aware Image Interpretation

Video Game Character Generation with Multiword Modulo Theories

Variational Learning of Probabilistic GeneratorsLearning a large class of estimators (e.g., Gaussian process models) is a challenging problem. For the past decade, there has been much interest in generating estimators that achieve consistent improvement. In this work, we consider the problem of learning an estimator for a large class of estimators. In this paper we propose a novel estimator for several large class of estimators including Markov chains and conditional random fields. We use a modified version of the Residual Recurrent Neural Network (RRCNN) model, which is able to learn a conditional probability density estimator from data, without relying on the input of any estimator. Our model achieves state-of-the-art performance and is able to achieve better performance with less computation with the same model complexity. We apply our algorithm to a variety of large data sets generated by Bayesian networks and to a large-scale model classification problem.