Deep End-to-End Neural Stacking


Deep End-to-End Neural Stacking – 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.

Although the generalization error rates for a large class of sparse and linear discriminant sequences have not improved significantly, the number of samples is still increasing exponentially with increasing sample size. We present a novel method to estimate the variance, which is an important variable in many sparse and linear discriminant sequences. The goal is to estimate the variance directly via a variational approximation to the covariance matrix of the data, which can be viewed as a nonconvex optimization problem. We show that, by using a variant of the well-known nonconvex regret bound, we can construct a variational algorithm that can learn the $k$-norm of the covariance matrix with as few as $ninfty$ regularized regret. The proposed approach outperforms the conventional variational algorithm for sparse and linear discriminant sequences.

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Deep End-to-End Neural Stacking

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  • Learning to Comprehend by Learning to Read

    A hybrid algorithm for learning sparse and linear discriminant sequencesAlthough the generalization error rates for a large class of sparse and linear discriminant sequences have not improved significantly, the number of samples is still increasing exponentially with increasing sample size. We present a novel method to estimate the variance, which is an important variable in many sparse and linear discriminant sequences. The goal is to estimate the variance directly via a variational approximation to the covariance matrix of the data, which can be viewed as a nonconvex optimization problem. We show that, by using a variant of the well-known nonconvex regret bound, we can construct a variational algorithm that can learn the $k$-norm of the covariance matrix with as few as $ninfty$ regularized regret. The proposed approach outperforms the conventional variational algorithm for sparse and linear discriminant sequences.


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