Evolving inhomogeneity in the presence of external noise using sparsity-based nonlinear adaptive interpolation


Evolving inhomogeneity in the presence of external noise using sparsity-based nonlinear adaptive interpolation – The paper presents a novel neural computational model combining deep learning with supervised learning. We propose a new model to capture discriminative temporal dynamics in a deep learning framework; by leveraging the structure of the recurrent network. The structure provides an efficient way of modeling the semantic domain, which makes the learning process extremely efficient. The model is evaluated on three challenging object detection benchmarks: VOT 2007-2012, VOT 2008-2010 and VOT 2017. The performance of the model compares favorably to both the baseline models and the state-of-the-art methods, as well as the recently proposed Recurrent Deep Network. In addition, the model is able to handle the semantic domain in a very light way. For instance, it outperforms the baseline model on several challenging object detection benchmark.

We propose the use of convolutional neural networks (CNNs) to learn information from images. Our proposed approach can be evaluated and compared to other works, with the latter performing more competitively in the image classification tasks by explicitly using a CNN architecture. It is worth mentioning that previous CNN architectures are built around the idea that the loss of information from non-linear inputs is minimized. In contrast, we show that this idea is exploited to better utilize the image features of the input images, which is a key capability of CNNs. The main contribution of our approach is to learn image-based representations from images. We present a novel representation learning approach that can be applied to an image classification task and outperforms previous CNN architectures for the task.

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Evolving inhomogeneity in the presence of external noise using sparsity-based nonlinear adaptive interpolation

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  • Learning an Optimal Transition Between Groups using Optimal Transition Parameters

    Sparse Clustering with Missing Data via the Adiabatic Greedy Mixture ModelWe propose the use of convolutional neural networks (CNNs) to learn information from images. Our proposed approach can be evaluated and compared to other works, with the latter performing more competitively in the image classification tasks by explicitly using a CNN architecture. It is worth mentioning that previous CNN architectures are built around the idea that the loss of information from non-linear inputs is minimized. In contrast, we show that this idea is exploited to better utilize the image features of the input images, which is a key capability of CNNs. The main contribution of our approach is to learn image-based representations from images. We present a novel representation learning approach that can be applied to an image classification task and outperforms previous CNN architectures for the task.


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