On top of existing computational methods for adaptive selection – Neural Network Modeling (NNM) is one of the largest science in the world and has been used extensively for many years. It is an important and essential problem since it is the main question of many applications, such as machine learning, information retrieval (IR) and medical diagnosis. In this paper, we present the first novel NNM method that incorporates knowledge gained from deep learning algorithms for a variety of tasks. Our model is able to learn a knowledge graph that consists of different nodes and a set of edges that the network is able to process. We evaluate our algorithm and show that it outperforms state-of-the-art neural network methods.

In this paper, we describe a deep learning (DL) framework for segmentation of the human hippocampus. The hippocampus is considered as a functional brain region that contains various sensory and motor functions. In this context, a neural network (NN) has received attention in recent years. However, the classification of the hippocampal region by an NN does not provide a good performance for the task, because of the limited number of labeled examples. Therefore, we propose an DL framework that takes a dataset of hippocampal data and models the information in the hippocampus as an optimization problem, using Deep Belief Networks (DBNs). The proposed framework, DeepDNN, enables a DL paradigm by learning nonlinear models of the hippocampus. Experiments on both synthetic and real-world data, and experiments using human and a dataset from the International Brain Project (IB) and the NIH NeuroImage Retinal Descent (RAED) datasets, demonstrate the efficacy of our DL system over the standard DeepDNN models.

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On top of existing computational methods for adaptive selection

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Deep End-to-End Neural StackingIn this paper, we describe a deep learning (DL) framework for segmentation of the human hippocampus. The hippocampus is considered as a functional brain region that contains various sensory and motor functions. In this context, a neural network (NN) has received attention in recent years. However, the classification of the hippocampal region by an NN does not provide a good performance for the task, because of the limited number of labeled examples. Therefore, we propose an DL framework that takes a dataset of hippocampal data and models the information in the hippocampus as an optimization problem, using Deep Belief Networks (DBNs). The proposed framework, DeepDNN, enables a DL paradigm by learning nonlinear models of the hippocampus. Experiments on both synthetic and real-world data, and experiments using human and a dataset from the International Brain Project (IB) and the NIH NeuroImage Retinal Descent (RAED) datasets, demonstrate the efficacy of our DL system over the standard DeepDNN models.