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.

Input: Consider Input: 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.

Output: Dropout Training as Adaptive Regularization

Input: Consider Input: In this paper, we model the trajectory of sea vessels and provide a service that predicts in near-real time the position of any given vessel in 4', 10', 20' and 40' time intervals. We explore the necessary tradeoffs between accuracy, performance and resource utilization are explored given the large volume and update rates of input data. We start with building models based on well-established machine learning algorithms using static datasets and multi-scan training approaches and identify the best candidate to be used in implementing a single-pass predictive approach, under real-time constraints. The results are measured in terms of accuracy and performance and are compared against the baseline kinematic equations. Results show that it is possible to efficiently model the trajectory of multiple vessels using a single model, which is trained and evaluated using an adequately large, static dataset, thus achieving a significant gain in terms of resource usage while not compromising accuracy.

Output: Employing traditional machine learning algorithms for big data streams analysis: the case of object trajectory prediction

Input: Consider Input: We propose Novel Object Captioner (NOC), a deep visual semantic captioning model that can describe a large number of object categories not present in existing image-caption datasets. Recent captioning models are limited in their ability to scale and describe concepts outside of paired image-text corpora. Our model takes advantage of external sources labeled images from object recognition datasets, and semantic knowledge extracted from unannotated text and combines them to generate descriptions about novel objects. We propose minimizing a joint objective which can learn from diverse data sources and leverage distributional semantic embeddings, enabling the model to generalize and describe novel objects outside of imagecaption datasets. We demonstrate that our model exploits semantic information to generate captions for hundreds of object categories in the ImageNet object recognition dataset that are not observed in imagecaption training data, as well as many categories that are observed very rarely.
Output: Captioning Images with Diverse Objects