Stereoscopic 2D: Semantics, Representation and Rendering


Stereoscopic 2D: Semantics, Representation and Rendering – We consider a novel problem: how to find a segmentation that best matches a given dataset given any data points? We propose a general learning algorithm. Our algorithm relies on the observation that most of the dataset is labeled and a large number of samples are missing. To alleviate the problem of missing data and of overfitting, we propose an efficient algorithm to simultaneously classify and reuse the labels of the labeled data. We show that our algorithm performs well in scenarios where the label space is sufficiently large, particularly for the most difficult cases. We also compare our algorithm to recent state-of-the-art deep learning models, including both synthetic and real data, on several benchmark datasets.

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.

MIDA: Multiple Imputation Models and Acceleration of Inference

An empirical evaluation of Bayesian ensemble learning for linear models

Stereoscopic 2D: Semantics, Representation and Rendering

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  • Distributed Stochastic Optimization for Conditional Random Fields

    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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