Learning to Learn Visual Representations with Spatial Recurrent Attention – We present the technique of combining the deep neural networks with recurrent neural networks, which allows us to extend the existing approaches to learn visual representations. We present four neural networks that encode the features of a given image as a sequence into vectors that are then applied to the images to produce images with similar visual properties. The learned representations are further fed to the recurrent neural networks via multiple back-propagation. Experiments on image retrieval are performed with state-of-the-art hand-crafted retrieval and recognition architectures.

An important question for solving large-scale optimization problems is how to estimate the distance between the optimal solutions and those predicted by prior estimators. Prior estimators for such queries assume prior learning which does not occur in the real world. In this tutorial, we develop and propose an efficient and effective estimator which is based on the prior structure and the estimation rules. The method is also well suited for the sparse set models as it can be used to estimate the posterior distribution of the optimal sample distribution. We demonstrate the applicability of the estimator on three benchmark datasets: (1) the MNIST dataset and (2) the MNIST dataset of the University of California-Berkeley. Our method can be applied to datasets of many types, including sparse and sparse-space models, and it is evaluated well on our large dataset, the UCB30K dataset, where the optimal estimate is close to the prior value.

A Hybrid Learning Framework for Discrete Graphs with Latent Variables

On the Computational Complexity of Deep Reinforcement Learning

Learning to Learn Visual Representations with Spatial Recurrent Attention

High Quality Video and Audio Classification using Adaptive Sampling

Online Variational Gaussian Process LearningAn important question for solving large-scale optimization problems is how to estimate the distance between the optimal solutions and those predicted by prior estimators. Prior estimators for such queries assume prior learning which does not occur in the real world. In this tutorial, we develop and propose an efficient and effective estimator which is based on the prior structure and the estimation rules. The method is also well suited for the sparse set models as it can be used to estimate the posterior distribution of the optimal sample distribution. We demonstrate the applicability of the estimator on three benchmark datasets: (1) the MNIST dataset and (2) the MNIST dataset of the University of California-Berkeley. Our method can be applied to datasets of many types, including sparse and sparse-space models, and it is evaluated well on our large dataset, the UCB30K dataset, where the optimal estimate is close to the prior value.