Fast Task Selection via Recurrent Residual Networks


Fast Task Selection via Recurrent Residual Networks – We propose to efficiently train recurrent multi-task learning models by using random walk (RK), and show that these models are more reliable and perform more efficiently than the state of the art. We apply our RK model to the learning of large-scale multilayer perceptrons, show that it outperforms other state of the art models.

In this manuscript we extend the existing classification algorithm with deep networks. We make use of recent deep architectures for supervised learning. The basic idea is to learn features from the data. Deep-learning networks are well suited to this task. Experiments with various datasets demonstrate that deep networks offer improved classification performance compared to state-of-the-art deep architectures.

Multi-view Graph Convolutional Neural Network

Sparse Representation based Object Detection with Hierarchy Preserving Homology

Fast Task Selection via Recurrent Residual Networks

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  • Learning Strict Partial Ordered Dependency Tree

    CNN-based Classification for Improved Automatic Pancreas ExtractionIn this manuscript we extend the existing classification algorithm with deep networks. We make use of recent deep architectures for supervised learning. The basic idea is to learn features from the data. Deep-learning networks are well suited to this task. Experiments with various datasets demonstrate that deep networks offer improved classification performance compared to state-of-the-art deep architectures.


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