The Effectiveness of Multitask Learning in Deep Learning Architectures


The Effectiveness of Multitask Learning in Deep Learning Architectures – We extend Deep neural networks with an architecture for machine learning of the network structure in a context of a spatial ordering. Our approach uses multiple layers of neural network, thus a network in a single layer could not be used for multiple tasks over a limited time horizon. More importantly, we focus on the problem with a spatial ordering of the network structures in a network architecture. In this work, we propose a model to learn a model of the spatial ordering of networks.

We present a simple model-free reinforcement learning method to successfully learn to exploit the structure of high-dimensional (HDS) data. Our method learns to maximize the cost of exploration and minimize the cost of exploration in a hierarchical, stochastic and high-dimensional framework, while simultaneously minimizing both the cost and time involved in exploration. The learned model is trained over a set of HDS-labels and then the network learns to exploit the HDS structure in the hierarchical framework, while the reward function is learned by stochastic optimization. By solving a large class of problems, our model learns to maximize the amount of reward while minimizing the amount of exploration while minimizing the time required to explore. We demonstrate experimental results on real-world datasets and benchmark datasets over the CIFAR and SIFT datasets, and our model outperforms other state-of-the-art approaches on the COCO and KITTI datasets.

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The Effectiveness of Multitask Learning in Deep Learning Architectures

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  • Learning to Learn Discriminatively-Learning Stochastic Grammars

    Deep Learning for Scalable Automatic Seizure DetectionWe present a simple model-free reinforcement learning method to successfully learn to exploit the structure of high-dimensional (HDS) data. Our method learns to maximize the cost of exploration and minimize the cost of exploration in a hierarchical, stochastic and high-dimensional framework, while simultaneously minimizing both the cost and time involved in exploration. The learned model is trained over a set of HDS-labels and then the network learns to exploit the HDS structure in the hierarchical framework, while the reward function is learned by stochastic optimization. By solving a large class of problems, our model learns to maximize the amount of reward while minimizing the amount of exploration while minimizing the time required to explore. We demonstrate experimental results on real-world datasets and benchmark datasets over the CIFAR and SIFT datasets, and our model outperforms other state-of-the-art approaches on the COCO and KITTI datasets.


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