Towards Optimal Cooperative and Efficient Hardware Implementations


Towards Optimal Cooperative and Efficient Hardware Implementations – We present the first approach that uses a neural network to learn a structured embeddings of complex input data without any prior supervision. The embedding consists of a structure over different classes of variables: variables in the input data can be either labelled as continuous variables or variable names can be generated by neural networks. Experiments show that the embedding model is able to extract such structure, i.e. we can infer how the complex data might fit in a structured model without making any pre-processing steps.

We propose an alternative to the traditional unsupervised clustering for supervised learning. This is a non-trivial choice due to the data structures that need to be defined, and the unknown labels needed. We propose a novel loss function, which learns to rank the label, and use this rank information to improve the performance of unlabeled data in the model. We show that our loss function is efficient and can be used to obtain more accurate classification performance than previous supervised clustering. We show that our loss function is non-trivially accurate on the data set in which it is used.

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Towards Optimal Cooperative and Efficient Hardware Implementations

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    Fast Label Propagation in Health Care Claims: Analysis and Future DirectionsWe propose an alternative to the traditional unsupervised clustering for supervised learning. This is a non-trivial choice due to the data structures that need to be defined, and the unknown labels needed. We propose a novel loss function, which learns to rank the label, and use this rank information to improve the performance of unlabeled data in the model. We show that our loss function is efficient and can be used to obtain more accurate classification performance than previous supervised clustering. We show that our loss function is non-trivially accurate on the data set in which it is used.


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