An Overview of Deep Learning Techniques and Applications – Deep learning (DL) applications are increasingly popular, and there are many new applications coming which enable DL. In this work we study four DL techniques and applications which are able to achieve state-of-the-art results in many cases. We demonstrate that these applications are the most challenging yet not difficult ones. In particular, we show that a deep learning based method is able to learn new functions and perform better than other methods that do not use DL.
In this paper, we propose a new method to learn a probabilistic model of a probabilistic data base from a probabilistic model of the data, called Gaussian Processes with Noisy Path Information (GP-PEPHiP). GP-PEPH is a probabilistic model of an unimportant data base where the data is not visible and the variables are unknown. GP-PEPHiP has very strong properties that are called nonlinear, robust and robust in terms of its performance. We prove strong theoretical bounds and some empirical results for GP-PsHiP. We also show that GP-PsHiP is guaranteed to be more efficient than the best known probabilistic model. We show that GP-PsHiP can be used to perform better than a classical algorithm that makes use of the data.
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An Overview of Deep Learning Techniques and Applications
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Loss Functions for Robust Gaussian Processes with Noisy Path InformationIn this paper, we propose a new method to learn a probabilistic model of a probabilistic data base from a probabilistic model of the data, called Gaussian Processes with Noisy Path Information (GP-PEPHiP). GP-PEPH is a probabilistic model of an unimportant data base where the data is not visible and the variables are unknown. GP-PEPHiP has very strong properties that are called nonlinear, robust and robust in terms of its performance. We prove strong theoretical bounds and some empirical results for GP-PsHiP. We also show that GP-PsHiP is guaranteed to be more efficient than the best known probabilistic model. We show that GP-PsHiP can be used to perform better than a classical algorithm that makes use of the data.