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.

Auto-encoders are perhaps the best-known non-probabilistic methods for representation learning. They are conceptually simple and easy to train. Recent theoretical work has shed light on their ability to capture manifold structure, and drawn connections to density modeling. This has motivated researchers to seek ways of auto-encoder scoring, which has furthered their use in classification. Gated auto-encoders (GAEs) are an interesting and flexible extension of auto-encoders which can learn transformations among different images or pixel covariances within images. However, they have been much less studied, theoretically or empirically. In this work, we apply a dynamical systems view to GAEs, deriving a scoring function, and drawing connections to Restricted Boltzmann Machines. On a set of deep learning benchmarks, we also demonstrate their effectiveness for single and multi-label classification.
Scoring and Classifying with Gated Auto-encoders

In this research we address the problem of capturing recurring concepts in a data stream environment. Recurrence capture enables the re-use of previously learned classifiers without the need for re-learning while providing for better accuracy during the concept recurrence interval. We capture concepts by applying the Discrete Fourier Transform (DFT) to Decision Tree classifiers to obtain highly compressed versions of the trees at concept drift points in the stream and store such trees in a repository for future use. Our empirical results on real world and synthetic data exhibiting varying degrees of recurrence show that the Fourier compressed trees are more robust to noise and are able to capture recurring concepts with higher precision than a meta learning approach that chooses to re-use classifiers in their originally occurring form.
Mining Recurrent Concepts in Data Streams using the Discrete Fourier Transform

Systematic use of the published results of randomized clinical trials is<lb>increasingly important in evidence-based medicine. In order to collate and<lb>analyze the results from potentially numerous trials, evidence tables are<lb>used to represent trials concerning a set of interventions of interest. An<lb>evidence table has columns for the patient group, for each of the interven-<lb>tions being compared, for the criterion for the comparison (e.g. proportion<lb>who survived after 5 years from treatment), and for each of the results.<lb>Currently, it is a labour-intensive activity to read each published paper<lb>and extract the information for each field in an evidence table. There have<lb>been some NLP studies investigating how some of the features from papers<lb>can be extracted, or at least the relevant sentences identified. However,<lb>there is a lack of an NLP system for the systematic extraction of each item<lb>of information required for an evidence table. We address this need by a<lb>combination of a maximum entropy classifier, and integer linear program-<lb>ming. We use the later to handle constraints on what is an acceptable<lb>classification of the features to be extracted. With experimental results,<lb>we demonstrate substantial advantages in using global constraints (such<lb>as the features describing the patient group, and the interventions, must<lb>occur before the features describing the results of the comparison).

Extraction of evidence tables from abstracts of randomized clinical trials using a maximum entropy classifier and global constraints