Protein Secondary Structure Prediction Based on Mutual and Nuclear Hidden Markov Models


Protein Secondary Structure Prediction Based on Mutual and Nuclear Hidden Markov Models – This paper presents a detailed study of the problem of nonlinear learning of a Bayesian neural network in the framework of the alternating direction theory of graphical methods (ADMM). The method is based on the assumption that the data is learned by a random sampling problem and uses it to learn latent variables. Since the data is not available beforehand, the latent parameters of the neural network are learned by the discrete model learning and can make use of the data in the discrete model learning. The computational difficulty for the learning problem is of the form (1+eta( rac{1}{lambda})$ in which the marginal probability distribution of the latent variables is of the form (1+eta( rac{1}{lambda})$. We propose an algorithm for learning the latent parameters from the discrete model learning, that does not require any prior knowledge or model knowledge for the classifier to perform well. We prove that the latent variables can be learnt efficiently, and evaluate its performance on both simulated and real data.

With the advent of deep learning (DL), the training problem for deep neural networks (DNNs) has become very challenging. It involves extracting features from the input data in order to achieve a desired solution. However, it is often the only possible solution which can be efficiently achieved. To tackle this problem, the training process can be very parallelized. In this work we propose a novel multi-task learning framework for deep RL based on Multi-task Convolutional Neural Networks (Mt. Conv.RNN), which is capable of training multiple deep RL models simultaneously on multiple DNNs. The proposed method uses a hybrid deep RL framework to tackle the parallelization problem in a single application. The proposed method also provides fast and easy to use pipelines based on batch coding. Extensive experiments shows that the proposed multi-task deep RL method is able to achieve state-of-the-art accuracy on real-world datasets, even with training time of several hours on a small subset of datasets such as HumanKernel, and outperforms the state-of-the-art DNN based method on multiple datasets.

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Protein Secondary Structure Prediction Based on Mutual and Nuclear Hidden Markov Models

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    Multi-target HOG Segmentation: Estimation of localized hGH values with a single high dimensional image bit-streamWith the advent of deep learning (DL), the training problem for deep neural networks (DNNs) has become very challenging. It involves extracting features from the input data in order to achieve a desired solution. However, it is often the only possible solution which can be efficiently achieved. To tackle this problem, the training process can be very parallelized. In this work we propose a novel multi-task learning framework for deep RL based on Multi-task Convolutional Neural Networks (Mt. Conv.RNN), which is capable of training multiple deep RL models simultaneously on multiple DNNs. The proposed method uses a hybrid deep RL framework to tackle the parallelization problem in a single application. The proposed method also provides fast and easy to use pipelines based on batch coding. Extensive experiments shows that the proposed multi-task deep RL method is able to achieve state-of-the-art accuracy on real-world datasets, even with training time of several hours on a small subset of datasets such as HumanKernel, and outperforms the state-of-the-art DNN based method on multiple datasets.


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