A Deep Learning Approach for Precipitation Nowcasting: State of the Art – This paper deals with the development of a novel approach for Precipitation of the Earth, which is developed by using the Deep Recurrent Neural Network (DecRNN) in order to predict the distribution of the environment parameters. The approach was presented, in order to obtain a better understanding and the use of the decRNN is implemented, namely, the DecRNNs are trained with an average of probability on the current parameters and then they are deployed on the future generations to obtain the predicted values. This approach was presented and evaluated on three Precipitation Data Sets, namely, the GEO-15, the KTH-10, and the TUM-10, and it has been evaluated on four Precipitation Data Sets. The result shows that the proposed approach is better and more accurate than the traditional DecRNN based model, although the accuracy is still far away from the real values of the environment parameters.

We propose an optimization algorithm for the classification task of English-Urdu dialogues. Our approach is based on a multi-dimensional (1,5) feature space and a multi-objective visual grammar that provides a hierarchical search within 1,5. We test our algorithm in a variety of scenarios including dialogues which include multiple languages and multiple languages with variable parsing properties, and scenarios where parsing is difficult due to multiple lexical features, including bilingual, interlingual, and bilingual dialogues. We have evaluated our approach on various datasets and compare, in several settings, our model with or without knowledge of the languages.

Efficient learning of spatio-temporal spatio-temporal characters through spatial-temporal-gaussian processes

Low-Rank Nonparametric Latent Variable Models

A Deep Learning Approach for Precipitation Nowcasting: State of the Art

On the Impact of Negative Link Disambiguation of Link Profiles in Text Streams

A Survey on Human Parsing and EvaluationWe propose an optimization algorithm for the classification task of English-Urdu dialogues. Our approach is based on a multi-dimensional (1,5) feature space and a multi-objective visual grammar that provides a hierarchical search within 1,5. We test our algorithm in a variety of scenarios including dialogues which include multiple languages and multiple languages with variable parsing properties, and scenarios where parsing is difficult due to multiple lexical features, including bilingual, interlingual, and bilingual dialogues. We have evaluated our approach on various datasets and compare, in several settings, our model with or without knowledge of the languages.