Deep Autoencoder: an Artificial Vision-Based Technique for Sensing of Sensor Data


Deep Autoencoder: an Artificial Vision-Based Technique for Sensing of Sensor Data – 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.

We propose a novel multi-valued structure approximation method for tree-cluster methods, which is the basis of modern nonlinear methods for the tree-cluster problem. The method iterates by computing two sub-sets of the tree-cluster data, one for each subset of features, and one for the sub-sets of the attributes. This method makes the trees more compact while reducing the number of features and attributes. To achieve this goal, we also propose an improved nonlinear optimization method called the multi-valued topological map optimization algorithm (MSA-OMP). The MSA-OMP algorithm uses a combination of both the tree-cluster and the attribute maps of the tree-clusters, and takes into account the relationship among the features and attributes in each subspace. Extensive experimentation has shown that the proposed method outperforms recent state-of-the-art tree-cluster methods such as the one presented by Zhang and Yao.

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Deep Autoencoder: an Artificial Vision-Based Technique for Sensing of Sensor Data

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  • Show, Help, Help: Learning and Drawing Multi-Modal Dialogue with Context Networks

    Optimal Topological Maps of Plant SpeciesWe propose a novel multi-valued structure approximation method for tree-cluster methods, which is the basis of modern nonlinear methods for the tree-cluster problem. The method iterates by computing two sub-sets of the tree-cluster data, one for each subset of features, and one for the sub-sets of the attributes. This method makes the trees more compact while reducing the number of features and attributes. To achieve this goal, we also propose an improved nonlinear optimization method called the multi-valued topological map optimization algorithm (MSA-OMP). The MSA-OMP algorithm uses a combination of both the tree-cluster and the attribute maps of the tree-clusters, and takes into account the relationship among the features and attributes in each subspace. Extensive experimentation has shown that the proposed method outperforms recent state-of-the-art tree-cluster methods such as the one presented by Zhang and Yao.


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