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


A Deep Learning Approach for Precipitation Nowcasting: State of the Art – This paper presents a novel method of learning an online model of human activity that can be leveraged for a variety of tasks, including video game, biomedical analysis and the prediction of weather events. The model automatically learns to predict future events. In this paper, the model is applied to model the prediction of the future weather conditions on a simulated environment and the prediction of the future weather conditions on a 3D object from a human viewpoint, allowing the model to model the world as a projected environment. The prediction of the future weather conditions on a simulated environment can be used to perform inference in an iterative manner to maximize expected value for future actions of the human model. Empirical evaluations on simulated and real world datasets have demonstrated that the model learns to predict the future weather conditions on the predicted environment while learning the state of the world, without the need to generate the predictions directly from a single data point.

We propose a new framework for efficient learning of Bayesian networks which is based on minimizing the posterior of the network with a fixed amount of information, and has the following properties: (1) it is NP-hard to approximate posterior estimates in the Bayesian space without using Bayes’ theorem for the posterior; (2) the method generalizes well to sparse networks; (3) the model can be used to learn the posterior on a high dimensional subspace on which Bayes’ theorem are embedded; (4) the method allows to adapt to new datasets, without needing an explicit prior. Our approach outperforms the existing methods in the literature by a significant margin.

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

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  • A Feature Based Deep Learning Recognition System For Indoor Action Recognition

    Fast Bayesian Clustering Algorithms using Approximate Logics with ApplicationsWe propose a new framework for efficient learning of Bayesian networks which is based on minimizing the posterior of the network with a fixed amount of information, and has the following properties: (1) it is NP-hard to approximate posterior estimates in the Bayesian space without using Bayes’ theorem for the posterior; (2) the method generalizes well to sparse networks; (3) the model can be used to learn the posterior on a high dimensional subspace on which Bayes’ theorem are embedded; (4) the method allows to adapt to new datasets, without needing an explicit prior. Our approach outperforms the existing methods in the literature by a significant margin.


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