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.

We provide an approach to learn latent vector representations at multiple scales, which in turn learns the latent representation of the data of interest over the data of interest. Our learning algorithm requires a finite size and a large number of samples, and is based on a convex minimization strategy. We provide a framework for the optimization of latent representation models for multiple scales by using a simple linear combination of the sparse representation and the latent vector. We generalize previous work on sparse representation models and show how to improve the classification accuracy of the representation by using both large sizes and large samples. Our algorithm is faster, but requires a larger number of samples and therefore is computationally harder to tune than previous methods. We present an efficient method to achieve this goal, and demonstrate that our algorithm achieves significantly better classification accuracy than existing methods.

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Recursive CNN: Bringing Attention to a Detail

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  • Predicting protein-ligand binding sites by deep learning with single-label sparse predictor learning

    Recurrent Online Prediction: A Stochastic ApproachWe provide an approach to learn latent vector representations at multiple scales, which in turn learns the latent representation of the data of interest over the data of interest. Our learning algorithm requires a finite size and a large number of samples, and is based on a convex minimization strategy. We provide a framework for the optimization of latent representation models for multiple scales by using a simple linear combination of the sparse representation and the latent vector. We generalize previous work on sparse representation models and show how to improve the classification accuracy of the representation by using both large sizes and large samples. Our algorithm is faster, but requires a larger number of samples and therefore is computationally harder to tune than previous methods. We present an efficient method to achieve this goal, and demonstrate that our algorithm achieves significantly better classification accuracy than existing methods.


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