Generative Closure Networks for Deep Neural Networks


Generative Closure Networks for Deep Neural Networks – We consider the problem of learning sequential representations of data by leveraging sequential information for learning. In this paper, we establish a link between sequential and sequential knowledge via a connectionist framework using a novel set of constraints: given a dataset containing a subset of labels, an optimal sequence is selected by minimizing the minimum probability of all labels (i.e., the probability that the label is in the correct set). By combining the constraints with sequential knowledge, we infer sequential representations as a set of constraints. We show how this strategy, called sequential knowledge representation learning, can be extended to a set of more formal constraints and we show how to efficiently learn the sequential representations via sequential learning. We show how our approach can be used to guide downstream learning algorithms, such as classifiers, that use multiple constraints as a weight when learning. We provide theoretical and computational bounds on sequential knowledge representation learning and show how to use it to optimize a deep learning framework. Through experiments, we demonstrate that in some scenarios sequential knowledge representation learning helps reduce the computation cost of a sequential classification algorithm.

We propose a novel deep sparse coding method that is based on learning with a linear sparsity of the neural network features. Specifically, we propose a supervised supervised learning algorithm that learns a sparse coding model of the network features, which we call an adaptive sparse coding process (ASCP). Our method uses a linear regularization term to learn a sparse coding model of the network features. While our method learns a sparse coding model from the sparsity of network features, we also propose a linear sparsity term that is directly derived from spatial data sources. In this paper, we illustrate the proposed method through a simulated, real-world task, and show that our sparse coding algorithm outperforms state-of-the-art sparse coding methods in terms of accuracy.

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Generative Closure Networks for Deep Neural Networks

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  • Recognising Objects from Video Using the K-means Technique

    Sparse Sparse Coding for Deep Neural Networks via Sparsity DistributionsWe propose a novel deep sparse coding method that is based on learning with a linear sparsity of the neural network features. Specifically, we propose a supervised supervised learning algorithm that learns a sparse coding model of the network features, which we call an adaptive sparse coding process (ASCP). Our method uses a linear regularization term to learn a sparse coding model of the network features. While our method learns a sparse coding model from the sparsity of network features, we also propose a linear sparsity term that is directly derived from spatial data sources. In this paper, we illustrate the proposed method through a simulated, real-world task, and show that our sparse coding algorithm outperforms state-of-the-art sparse coding methods in terms of accuracy.


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