Deep Learning with Deep Hybrid Feature Representations – 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.

This paper describes a neural network-based deep learning framework for the mapping of geometric patterns. The method first uses a deep neural network to automatically represent the geometric patterns. The network is trained to infer patterns from Euclidean distances. The network is then trained to generate geometric patterns and is then integrated with a convolutional neural network (CNN) to learn the geometry of the geometric patterns from a deep graph. The graph is then used as a regularization term to obtain a global topological map. The method was evaluated on the ImageNet dataset which shows that its accuracy to recognize the geometric patterns can be improved by 3.3%.

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# Deep Learning with Deep Hybrid Feature Representations

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The Global Topological Map Refinement AlgorithmThis paper describes a neural network-based deep learning framework for the mapping of geometric patterns. The method first uses a deep neural network to automatically represent the geometric patterns. The network is trained to infer patterns from Euclidean distances. The network is then trained to generate geometric patterns and is then integrated with a convolutional neural network (CNN) to learn the geometry of the geometric patterns from a deep graph. The graph is then used as a regularization term to obtain a global topological map. The method was evaluated on the ImageNet dataset which shows that its accuracy to recognize the geometric patterns can be improved by 3.3%.