Learning and Querying Large Graphs via Active Hierarchical Reinforcement Learning – We present a framework for learning deep neural networks by optimizing a set of parameters. Our framework achieves state of the art performance on several image datasets including PASCAL 2014, CIFAR-10
Deep learning has recently been studied as a highly challenging field which has attracted impressive amounts of attention. Many of its challenges, such as the difficulty of learning and its computational complexity, have been overcome in recent years. In this paper, we explore the problem of learning a neural network from raw pixel sets. As a result, our framework was able to solve the above problems with ease. We propose a method for an efficient learning of a neural network which can be used to adapt to different types of images. We use convolutional neural networks to learn an approximate representation of a pixel set consisting of the relevant semantic information. The model is then used to predict its output. We show empirically that the learned representation performs better than the pixel set prediction and this can easily be improved by training a different model.
Learning Bayesian networks (BNs) in a Bayesian context is a challenging problem with significant difficulty due to the high computational overhead. This work tackles this problem by learning from the input data sets and by leveraging the fact that the underlying Bayesian network representations are generated using a nonparametric, random process. We show that the network representations for both Gaussian and Bayesian networks achieve similar performance compared to the classical Bayesian network representations, including the Gaussian model and the nonparametric Bayesian model. In particular, we show that the Gaussian model performs significantly better than the nonparametric Bayesian model when the input data set includes only the Gaussian model.
Avalon: Towards a Database to Generate Traditional Arabic Painting Instructions
Proximal Methods for the Nonconvexized Proximal Product Surfaces
Learning and Querying Large Graphs via Active Hierarchical Reinforcement Learning
Ranking from Observational Data by Using Bags
A note on the lack of convergence for the generalized median classifierLearning Bayesian networks (BNs) in a Bayesian context is a challenging problem with significant difficulty due to the high computational overhead. This work tackles this problem by learning from the input data sets and by leveraging the fact that the underlying Bayesian network representations are generated using a nonparametric, random process. We show that the network representations for both Gaussian and Bayesian networks achieve similar performance compared to the classical Bayesian network representations, including the Gaussian model and the nonparametric Bayesian model. In particular, we show that the Gaussian model performs significantly better than the nonparametric Bayesian model when the input data set includes only the Gaussian model.