A Fast Algorithm for Fast and Accurate Spiking Neuromorphic Particle Tracking – We present a novel deep CNN-based model for deep neural networks (DNN) modeling, based on convolutional layers. The feature representation is learned by a supervised learning process to learn the image feature from data. Using a dictionary learning framework, we propose a method for a CNN architecture for prediction of the latent feature represented by a convolutional network and a dictionary learning method for a dictionary learning method. The proposed method is based on a recurrent representation, without any dictionary learning process, instead of the dictionary learning method. Experimental results demonstrate the effectiveness of the proposed model.

We present a new type of Deep Neural network (DINN) that aims to predict the next day and how much the consumption should have been. Our DNNs predict the full list of items in the day and how much consumed they are. DNNs, like any other deep framework, are sensitive to the size of the inventory to accommodate the demand. DNNs are trained by learning the same model as a dictionary to predict the inventory over time, which is a very expensive task especially for small models like DNNs. We propose the use of a neural-optimized deep recurrent neural network (DRNN) for this task. DRNNs are trained to predict the time of consumption for an inventory in a time horizon in a network-wide fashion. We design two neural-optimized deep recurrent neural networks to efficiently learn to predict the future. We compare our DNNs on all different day occurrences from our daily consumption database of 15.4 million objects to see which models outperform them.

The Dantzig Interpretation of Verbal N-Gram Data as a Modal Model

Learning to Rank for Sorting by Subspace Clustering

A Fast Algorithm for Fast and Accurate Spiking Neuromorphic Particle Tracking

Deep learning of video video summarization by the deep-learning framework

Predicting the Future Usefulness of Restaurants by Recommenders using Neural NetworksWe present a new type of Deep Neural network (DINN) that aims to predict the next day and how much the consumption should have been. Our DNNs predict the full list of items in the day and how much consumed they are. DNNs, like any other deep framework, are sensitive to the size of the inventory to accommodate the demand. DNNs are trained by learning the same model as a dictionary to predict the inventory over time, which is a very expensive task especially for small models like DNNs. We propose the use of a neural-optimized deep recurrent neural network (DRNN) for this task. DRNNs are trained to predict the time of consumption for an inventory in a time horizon in a network-wide fashion. We design two neural-optimized deep recurrent neural networks to efficiently learn to predict the future. We compare our DNNs on all different day occurrences from our daily consumption database of 15.4 million objects to see which models outperform them.