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Firstly, we divide the city into independent regions, in each of which a constant number of couriers deliver parcels and serve requests cooperatively. We present a reformulation of the distance metric learning problem as a penalized optimization problem, with a penalty term corresponding to the von Neumann entropy of the distance metric. The goal is to cluster the points to minimize the classical clustering cost but with the additional constraint that no color is over-represented in any cluster. Correspondingly, our approach leads to an analytical solution of the optimization problem based on the Boltzmann distribution. However, existing works fail to do that. We then develop a survival analysis framework to learn the differential equations of a dynamical system from its cross-sectional states.

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ARU-Nine (ARUMO N Scale)

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We also propose a scalable and effective algorithm for fitting and forecasting time-series data streams.

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Kimie Oharu

While most existing factorization models assume a multilinearity relationship between tensor entries and their corresponding factors, real world tensors tend to have more complex interactions than multilinearity. In many complex domains, the input data are often not suited for the typical vector representations used in deep learning models. In particular, this paradigm includes a substructure detector and an adversarial trainer. Empirical results demonstrate that the proposed deep learning algorithm outperforms all other state-of-the-art methods on most of metrics.

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