Multi-dimensional representation learning for word retrieval


Multi-dimensional representation learning for word retrieval – We develop a new, yet complementary method of learning an attention-based topic model (DASMM) for multi-dimensional representations. Traditionally, the task of representing a new topic is treated as a single- or multiple-dimensional representation task. We solve the multi-dimensional representation learning problem in an alternating direction method of learning the topic labels, and show that even a single word can significantly help in learning a multi-dimensional representation. We prove that the learning problem can be solved efficiently via the non-convex convex relaxation of the convex relaxation equation over multi-dimensional representations. In a large amount of experiments, we evaluate the effectiveness of our method in various domains and show the benefits of this method.

We study the problem of recovering and repairing small vessels of an unknown size. We present an initial solution using an iterative process to find the most likely position of vessel with the highest probability of success. We show that this process significantly reduces recovery time. Further, we show that this problem can be solved efficiently with a convolutional network. We further illustrate our approach by showing that it provides an effective tool to perform analysis and repair of vessels.

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Multi-dimensional representation learning for word retrieval

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  • Scalable Algorithms for Learning Low-rank Mixtures with Large-Margin Classification

    Recovery of Stochastic Vessels from Accelerating External StimulationWe study the problem of recovering and repairing small vessels of an unknown size. We present an initial solution using an iterative process to find the most likely position of vessel with the highest probability of success. We show that this process significantly reduces recovery time. Further, we show that this problem can be solved efficiently with a convolutional network. We further illustrate our approach by showing that it provides an effective tool to perform analysis and repair of vessels.


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