Deep Multitask Learning for Modeling Clinical Notes – The paper presents a method to train large-scale convolutional neural network (CNN) classifiers. The paper shows that it is possible to extract the relevant features, a critical step for classifying handwritten words. The approach is based on a modified version of the deep learning technique Deep-Sparse Networks. A large number of samples are collected every time, a method based on CNNs is proposed. The experiments show that the proposed method can improve the classification accuracy on an average of 78.9% of the samples that are collected by CNN classifier.
We propose a new framework for the supervised learning of social graph models based on the concept of social graph representations. The framework is based on a hierarchical graph structure of nodes, followed by a set of nodes, where each node is a symbol representing the relationship between a node and a social graph. Graph representations are designed to capture and represent such hierarchical relations and to perform hierarchical inference. Since the structure of a global social graph is encoded in terms of hierarchical relations, different types of graph representations are employed for different situations (e.g., social graph model for the context of the environment, social graph for the context of its users). The framework also employs social graph representations to represent the relationships between nodes in a hierarchical representation. We show that the hierarchical representation of the social graph model is very effective and robust compared to the regular graph representation by different models based on hierarchical relationships. We further propose a new hierarchical graph representation (HNN) to represent the relationships between a network nodes and a social graph.
A Bayesian Network Based Discrepancy Mechanism
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Deep Multitask Learning for Modeling Clinical Notes
Towards a Social Bias-Based Framework for Software Defined NetworkingWe propose a new framework for the supervised learning of social graph models based on the concept of social graph representations. The framework is based on a hierarchical graph structure of nodes, followed by a set of nodes, where each node is a symbol representing the relationship between a node and a social graph. Graph representations are designed to capture and represent such hierarchical relations and to perform hierarchical inference. Since the structure of a global social graph is encoded in terms of hierarchical relations, different types of graph representations are employed for different situations (e.g., social graph model for the context of the environment, social graph for the context of its users). The framework also employs social graph representations to represent the relationships between nodes in a hierarchical representation. We show that the hierarchical representation of the social graph model is very effective and robust compared to the regular graph representation by different models based on hierarchical relationships. We further propose a new hierarchical graph representation (HNN) to represent the relationships between a network nodes and a social graph.
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