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.

Recent advances in deep neural networks have enabled us to learn from sensory input data. Due to these new challenges, previous approaches have relied on either static representations of data or explicit knowledge of the underlying network structure. In this work, we propose a novel method based on deep representations learning. Specifically, we propose a method involving simultaneous knowledge and memory of a learned representation from a sensor data. We first learn the underlying model as a single image from the sensors. Next, we map the learned representation to the model’s representation space. In contrast to a traditional learning-based approach, our method exploits knowledge sharing between model instances. Moreover, by using a network of latent representations of data, we develop a novel generalization of the concept of deep memory. We propose a framework of deep neural networks that learns a model from input data and then maps the model onto new representations when given a new one. Our theoretical analysis shows that by using different representations, such as discrete representations, the learned model learns to discriminate the input image from the model. We show that a method based on deep representations learning can outperform baselines.

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

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    Deep Autoencoder: an Artificial Vision-Based Technique for Sensing of Sensor DataRecent advances in deep neural networks have enabled us to learn from sensory input data. Due to these new challenges, previous approaches have relied on either static representations of data or explicit knowledge of the underlying network structure. In this work, we propose a novel method based on deep representations learning. Specifically, we propose a method involving simultaneous knowledge and memory of a learned representation from a sensor data. We first learn the underlying model as a single image from the sensors. Next, we map the learned representation to the model’s representation space. In contrast to a traditional learning-based approach, our method exploits knowledge sharing between model instances. Moreover, by using a network of latent representations of data, we develop a novel generalization of the concept of deep memory. We propose a framework of deep neural networks that learns a model from input data and then maps the model onto new representations when given a new one. Our theoretical analysis shows that by using different representations, such as discrete representations, the learned model learns to discriminate the input image from the model. We show that a method based on deep representations learning can outperform baselines.


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