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


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 a novel and effective method to automatically learn the basis of models’ beliefs from images. We first show that the assumption of beliefs is a necessary condition for learning a model from images. Second, we propose an algorithm to learn the basis of model’s belief. Lastly, we use a novel, simple, and effective feature-based approach to learn the belief structure of models. These features, together with semantic information we provide on model’s beliefs, allow us to generalize the framework to the many domains with better generalizations. Our model is trained end-to-end using a state-of-the-art neural network that we have used for training.

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

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  • On the Computability of CNN Features for Identifying Prostate Cancer Clinical Trials Using Single Shot CNN

    Proceedings of the 2010 ICML Workshop on Disbelief in Artificial Intelligence (W3 2010)We propose a novel and effective method to automatically learn the basis of models’ beliefs from images. We first show that the assumption of beliefs is a necessary condition for learning a model from images. Second, we propose an algorithm to learn the basis of model’s belief. Lastly, we use a novel, simple, and effective feature-based approach to learn the belief structure of models. These features, together with semantic information we provide on model’s beliefs, allow us to generalize the framework to the many domains with better generalizations. Our model is trained end-to-end using a state-of-the-art neural network that we have used for training.


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