Recursive CNN: Bringing Attention to a Detail


Recursive CNN: Bringing Attention to a Detail – In the early days of Machine Learning (ML), attention was a key ingredient to improve performance. A key task to address was to automatically recognize semantic and object categories. In this paper, we consider this task to be represented by a deep neural network and use it as a part of an attention model for classification. In our approach, we explore the idea of the attention model to learn to track semantic categories for objects and the category models that are associated with the objects. The attention model was trained to automatically recognize the semantic categories at the top of the class list. We then evaluate the performance of different kinds of attention models when we are given examples with different categories. The accuracy of the model is increased by using the attention model during evaluation at the top of each category. The results show that when using the attention model we are better able to distinguish those categories of different types of categories.

In this paper, we propose a new method on the training of stochastic recurrent neural networks with sparse features. We use the sparse embedding as a model (in this case sparse vector) to represent the model-related features. We use a new sparse representation of the hidden structure of the network as a vector. In the supervised learning setting, we only need to use the sparsity of its representation for the classification task in order to train the stochastic network. This allows learning and prediction in a more natural way. The proposed method is based on the Sparse embedding of the network. We observe that the sparse representation performs well in the supervised learning setting, although it is more robust.

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Recursive CNN: Bringing Attention to a Detail

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  • Sparse Nonparametric MAP Inference

    Recurrent Convolutional Neural Network with Sparse Stochastic Contexts for Adversarial PredictionIn this paper, we propose a new method on the training of stochastic recurrent neural networks with sparse features. We use the sparse embedding as a model (in this case sparse vector) to represent the model-related features. We use a new sparse representation of the hidden structure of the network as a vector. In the supervised learning setting, we only need to use the sparsity of its representation for the classification task in order to train the stochastic network. This allows learning and prediction in a more natural way. The proposed method is based on the Sparse embedding of the network. We observe that the sparse representation performs well in the supervised learning setting, although it is more robust.


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