Learning to Cure a Kidney with Reinforcement Learning


Learning to Cure a Kidney with Reinforcement Learning – We propose a novel Neural Machine Translation (NMT) method to solve a kidney classification problem. We first show that the proposed method can achieve a good classification performance without using a huge amount of training data. Moreover, we propose and test a novel method where the NMT agent can extract different words from the training data. We also show that the proposed technique significantly outperforms the previous ones to a large degree.

The recent release of Convolutional Neural Networks (CNN) with deep architectures can be easily implemented, but is computationally expensive to train. Recent work has shown that the amount of data needed for training CNNs can be increased with the number of parameters used by hand. In this paper, we propose to address this problem by optimizing the CNNs’ parameters, but, in this case, they will not have access to the dictionary representation of the input data. We then propose a new algorithm, called SDS-CNN, which is able to optimize the parameters in a single run of training. Our algorithm requires only the dimension of the dataset, but reduces the training data by $O(sqrt(D))$ steps. The complexity of our algorithm is reduced to $O(sqrt{D})$ steps on average on average over each iteration. In our experiments, our algorithm runs almost twice faster than the baseline CNN, which is compared to $O(sqrt{D})$ steps. Our method can effectively be used, among its competitors, for various machine learning applications.

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Learning to Cure a Kidney with Reinforcement Learning

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    Segmentation from High Dimensional Data using Gaussian Process Network LassoThe recent release of Convolutional Neural Networks (CNN) with deep architectures can be easily implemented, but is computationally expensive to train. Recent work has shown that the amount of data needed for training CNNs can be increased with the number of parameters used by hand. In this paper, we propose to address this problem by optimizing the CNNs’ parameters, but, in this case, they will not have access to the dictionary representation of the input data. We then propose a new algorithm, called SDS-CNN, which is able to optimize the parameters in a single run of training. Our algorithm requires only the dimension of the dataset, but reduces the training data by $O(sqrt(D))$ steps. The complexity of our algorithm is reduced to $O(sqrt{D})$ steps on average on average over each iteration. In our experiments, our algorithm runs almost twice faster than the baseline CNN, which is compared to $O(sqrt{D})$ steps. Our method can effectively be used, among its competitors, for various machine learning applications.


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