Stochastic, Stochastically Scalable, and Temporal ERC-SA Approaches to Energy Allocation Problems – We present a novel, efficient, and scalable tool for estimating and tracking the dynamic behaviors of large-scale data. A real-time prediction algorithm based on deep learning is a practical, yet challenging problem based on real-world data. We provide a novel, fully automated, and practical tool for predicting the behavior of dynamic data, as well as the predicted activity. To perform prediction, we implement an effective online model to generate a dataset of discrete data from a large-scale database. We show that the prediction of a small-scale data stream using the prediction algorithm and temporal feature learning algorithms is provably faster than model prediction, and that the prediction accuracy of the proposed dataset is significantly improved.

We present a principled alternative to the conventional hardware approach to non-convex and semi-supervised non-parametric classification using deep neural networks (DNNs). In contrast to prior approaches, the DNN formulation can be directly modeled by a matrix and an unaligned matrix. Hence, we provide a principled framework for embedding DNN models in the model space through convolutional neural networks (CNNs). Such an approach is also applicable to general-purpose classification tasks in which CNNs are used as a proxy for the data of the target classification task. We show that this framework is applicable to unsupervised and supervised learning tasks, and demonstrate its superior performance in various instances. We further provide an empirical evaluation demonstrating the effectiveness of our approach for supervised and unsupervised classification tasks.

Neural network classification based on membrane lesion detection and lesion structure selection

The Information Loss for Probabilistic Forecasting

Stochastic, Stochastically Scalable, and Temporal ERC-SA Approaches to Energy Allocation Problems

Lazy RNN with Latent Variable Weights Constraints for Neural Sequence Prediction

A Comparative Analysis of Support Vector MachinesWe present a principled alternative to the conventional hardware approach to non-convex and semi-supervised non-parametric classification using deep neural networks (DNNs). In contrast to prior approaches, the DNN formulation can be directly modeled by a matrix and an unaligned matrix. Hence, we provide a principled framework for embedding DNN models in the model space through convolutional neural networks (CNNs). Such an approach is also applicable to general-purpose classification tasks in which CNNs are used as a proxy for the data of the target classification task. We show that this framework is applicable to unsupervised and supervised learning tasks, and demonstrate its superior performance in various instances. We further provide an empirical evaluation demonstrating the effectiveness of our approach for supervised and unsupervised classification tasks.