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.

Multiphoton Mass spectrometry data synthesis is a new method for identifying the presence of heterogeneous molecular structures in a set of images. Here we propose applying the method on real data to find the heterogeneous regions with very high heterogeneity. The proposed method is based on the theory the inter- and intra-differential analysis of the molecules (particle complexes) and the statistical analysis of the observed data, which have a variety of characteristics that distinguish them from heterogeneous regions. We show that the proposed method is able to detect the presence of the complex structures and therefore provide better classification results than existing ones for multiphoton mass spectrometry. By using the proposed model, many multiphoton mass spectrometers can be considered. Results show that the proposed method can reach competitive performance compared to other state-of-the-art methods based on the clustering and annotation techniques.

An Analysis of A Simple Method for Clustering Sparsely

Theoretical Foundations for Machine Learning on the Continuous Ideal Space

Learning to Cure a Kidney with Reinforcement Learning

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  • Towards Large-grained Visual Classification by Optimizing for Hierarchical Feature Learning

    Multiphoton Mass Spectrometry Data Synthesis for Clonal Antigen DetectionMultiphoton Mass spectrometry data synthesis is a new method for identifying the presence of heterogeneous molecular structures in a set of images. Here we propose applying the method on real data to find the heterogeneous regions with very high heterogeneity. The proposed method is based on the theory the inter- and intra-differential analysis of the molecules (particle complexes) and the statistical analysis of the observed data, which have a variety of characteristics that distinguish them from heterogeneous regions. We show that the proposed method is able to detect the presence of the complex structures and therefore provide better classification results than existing ones for multiphoton mass spectrometry. By using the proposed model, many multiphoton mass spectrometers can be considered. Results show that the proposed method can reach competitive performance compared to other state-of-the-art methods based on the clustering and annotation techniques.


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