Combining Multi-Dimensional and Multi-DimensionalLS Measurements for Unsupervised Classification – State-of-the-art deep CNNs are characterized by a high number of feature vector representations that are used to train a single model model for a given task. Moreover, a wide variety of tasks in artificial and real life applications can be learned simultaneously with a single deep model. In this paper, we propose a novel approach for jointly learning features and deep networks by using joint representations of different dimensions such as the convolutional, convolutional, or multi-dimensional. Unlike traditional CNNs, which only learn the features in the convolutional layers, we can learn the features on the convolutional layers without any prior knowledge about the data of interest. We demonstrate that the proposed approach outperforms the state-of-the-art deep CNNs on several benchmark datasets that are difficult to be trained by traditional CNNs.
We study the problem of learning a graph-tree structure from graph data under an arbitrary number of constraints. The algorithm involves a stochastic optimization algorithm and a finite number of iterations, which are computationally expensive; this can be a huge burden for non-experts. We use a stochastic optimization algorithm that is well known in the literature for solving this optimization problem, and give a theoretical analysis that shows that the algorithm converges to the optimal solution and thus is efficient. We also show that the algorithm improves on the state-of-the-art stochastic stochastic optimization solvers by a small margin.
Learning Graph from Data in ALC
Combining Multi-Dimensional and Multi-DimensionalLS Measurements for Unsupervised Classification
Automated Evaluation of Neural Networks for Polish Machine-Patch Recognition
Invertible Stochastic Approximation via Sparsity Reduction and Optimality PursuitWe study the problem of learning a graph-tree structure from graph data under an arbitrary number of constraints. The algorithm involves a stochastic optimization algorithm and a finite number of iterations, which are computationally expensive; this can be a huge burden for non-experts. We use a stochastic optimization algorithm that is well known in the literature for solving this optimization problem, and give a theoretical analysis that shows that the algorithm converges to the optimal solution and thus is efficient. We also show that the algorithm improves on the state-of-the-art stochastic stochastic optimization solvers by a small margin.