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.

Ex Input:
Multi Expression Programming (MEP) is an evolutionary technique that may be used for solving computationally difficult problems. MEP uses a linear solution representation. Each MEP individual is a string encoding complex expressions (computer programs). A MEP individual may encode multiple solutions of the current problem. In this paper MEP is used for evolving a Traveling Salesman Problem (TSP) heuristic for graphs satisfying triangle inequality. Evolved MEP heuristic is compared with Nearest Neighbor Heuristic (NN) and Minimum Spanning Tree Heuristic (MST) on some difficult problems in TSPLIB. For most of the considered problems the evolved MEP heuristic outperforms NN and MST. The obtained algorithm was tested against some problems in TSPLIB. The results emphasizes that evolved MEP heuristic is a powerful tool for solving difficult TSP instances.

Ex Output:
Evolving TSP heuristics using Multi Expression Programming


Ex Input:
Experimenting with a new dataset of 1.6M user comments from a Greek news portal and existing datasets of English Wikipedia comments, we show that an RNN outperforms the previous state of the art in moderation. A deep, classification-specific attention mechanism improves further the overall performance of the RNN. We also compare against a CNN and a word-list baseline, considering both fully automatic and semi-automatic moderation.

Ex Output:
Deep Learning for User Comment Moderation


Ex Input:
Energy-based models are popular in machine learning due to the elegance of their formulation and their relationship to statistical physics. Among these, the Restricted Boltzmann Machine (RBM) has been the prototype for some recent advancements in the unsupervised training of deep neural networks. However, the contrastive divergence training algorithm, so often used for such models, has a number of drawbacks and ineligancies both in theory and in practice. Here, we investigate the performance of Minimum Probability Flow learning for training RBMs. This approach reconceptualizes the nature of the dynamics defined over a model, rather than thinking about Gibbs sampling, and derives a simple, tractable, and elegant objective function using a Taylor expansion, allowing one to learn the parameters of any distribution over visible states. In the paper, we expound the Minimum Probability Flow learning algorithm under various dynamics. We empirically analyze its performance on these dynamics and demonstrate that MPF algorithms outperform CD on various RBM configurations.

Ex Output:

UNDERSTANDING MINIMUM PROBABILITY FLOW FOR RBMS UNDER VARIOUS KINDS OF DYNAMICS