Efficient Orthogonal Graphical Modeling on Data


Efficient Orthogonal Graphical Modeling on Data – Semantic similarity aims at ranking and categorising the pairwise similarities. To tackle queries such as: 1) ranking or categorising a given pair, 2) grouping pair pairs of related items and 3) the grouping of their groups, we need to learn to rank them to obtain the best pairwise similarity. One approach is to take a pair as a global metric. Then, we consider the query of the query in the global metric and find its optimal score by searching for the best pair (i.e., the optimal score matches the query rank).

Deep Neural Network (DNN) has emerged as a powerful tool for the analysis of neural network data. In this work, we explore deep learning-based methods to automatically segment neural networks based on their functional connectivity patterns. In this process, we consider the possibility to model the network structure of its neural network by analyzing the connectivity patterns on each module. We show that network structure is critical for segmentation of neural networks. The functional connectivity patterns on each module can be modeled by a weighted kernel which is a well known technique in the literature. We propose a method which integrates the functional connectivity patterns and the spatial information in each node by modeling the spatial network structure using functional connectivity functions. Our model-based approach is shown to have superior performance compared to a variety of network segmentation methods.

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Efficient Orthogonal Graphical Modeling on Data

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  • The Information Bottleneck Problem with Finite Mixture Models

    Deep Learning with Deep Hybrid Feature RepresentationsDeep Neural Network (DNN) has emerged as a powerful tool for the analysis of neural network data. In this work, we explore deep learning-based methods to automatically segment neural networks based on their functional connectivity patterns. In this process, we consider the possibility to model the network structure of its neural network by analyzing the connectivity patterns on each module. We show that network structure is critical for segmentation of neural networks. The functional connectivity patterns on each module can be modeled by a weighted kernel which is a well known technique in the literature. We propose a method which integrates the functional connectivity patterns and the spatial information in each node by modeling the spatial network structure using functional connectivity functions. Our model-based approach is shown to have superior performance compared to a variety of network segmentation methods.


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