In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Q: Knowledge graph embedding aims to embed entities and relations of knowledge graphs into low-dimensional vector spaces. Translating embedding methods regard relations as the translation from head entities to tail entities, which achieve the state-of-the-art results among knowledge graph embedding methods. However, a major limitation of these methods is the time consuming training process, which may take several days or even weeks for large knowledge graphs, and result in great diculty in practical applications. In this paper, we propose an ecient parallel framework for translating embedding methods, called ParTrans-X, which enables the methods to be paralleled without locks by utilizing the distinguished structures of knowledge graphs. Experiments on two datasets with three typical translating embedding methods, i.e., TransE [3], TransH [19], and a more ecient variant TransEAdaGrad [11] validate that ParTrans-X can speed up the training process by more than an order of magnitude.

A: Eicient Parallel Translating Embedding For Knowledge Graphs
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Q: Dropout and other feature noising schemes control overfitting by artificially cor-<lb>rupting the training data. For generalized linear models, dropout performs a form<lb>of adaptive regularization. Using this viewpoint, we show that the dropout regular-<lb>izer is first-order equivalent to an L2 regularizer applied after scaling the features<lb>by an estimate of the inverse diagonal Fisher information matrix. We also establish<lb>a connection to AdaGrad, an online learning algorithm, and find that a close rel-<lb>ative of AdaGrad operates by repeatedly solving linear dropout-regularized prob-<lb>lems. By casting dropout as regularization, we develop a natural semi-supervised<lb>algorithm that uses unlabeled data to create a better adaptive regularizer. We ap-<lb>ply this idea to document classification tasks, and show that it consistently boosts<lb>the performance of dropout training, improving on state-of-the-art results on the<lb>IMDB reviews dataset.

A: Dropout Training as Adaptive Regularization
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Q: In this paper a new heuristic optimization algorithm has been introduced based on the performance of the major football leagues within each season in EU countries. The algorithm starts with an initial population including three different groups of teams: the wealthiest (strongest), the regular, the poorest (weakest). Each individual of population constitute a football team while each player is an indication of a player in a post. The optimization can hopefully occurs when the competition among the teams in all the leagues is imitated as the strongest teams usually purchase the best players of the regular teams and in turn, regular teams purchase the best players of the weakest who should always discover young players instead of buying professionals. It has been shown that the algorithm can hopefully converge to an acceptable solution solving various benchmarks.

A:
Soccer League Optimization: A heuristic Algorithm Inspired by the Football System in European Countries
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