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.

Example Input: Eliminating the negative effect of highly non-stationary environmental noise is a long-standing research topic for speech recognition but remains an important challenge nowadays. To address this issue, traditional unsupervised signal processing methods seem to have touched the ceiling. However, data-driven based supervised approaches, particularly the ones designed with deep learning, have recently emerged as potential alternatives. In this light, we are going to comprehensively summarise the recently developed and most representative deep learning approaches to deal with the raised problem in this article, with the aim of providing guidelines for those who are going deeply into the field of environmentally robust speech recognition. To better introduce these approaches, we categorise them into singleand multi-channel techniques, each of which is specifically described at the front-end, the back-end, and the joint framework of speech recognition systems. In the meanwhile, we describe the pros and cons of these approaches as well as the relationships among them, which can probably benefit future research.
Example Output: Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments

Example Input: We introduce a parametric nonlinear transformation that is well-suited for Gaussianizing data from natural images. After a linear transformation of the data, each component is normalized by a pooled activity measure, computed by exponentiating a weighted sum of rectified and exponentiated components and an additive constant. We optimize the parameters of this transformation (linear transform, exponents, weights, constant) over a database of natural images, directly minimizing the negentropy of the responses. We find that the optimized transformation successfully Gaussianizes the data, achieving a significantly smaller mutual information between transformed components than previous methods including ICA and radial Gaussianization. The transformation is differentiable and can be efficiently inverted, and thus induces a density model on images. We show that samples of this model are visually similar to samples of natural image patches. We also demonstrate the use of the model as a prior density in removing additive noise. Finally, we show that the transformation can be cascaded, with each layer optimized (unsupervised) using the same Gaussianization objective, to capture additional probabilistic structure.
Example Output: GENERALIZED NORMALIZATION TRANSFORMATION

Example Input: <lb>This paper introduces AdaSDCA: an adap-<lb>tive variant of stochastic dual coordinate as-<lb>cent (SDCA) for solving the regularized empir-<lb>ical risk minimization problems. Our modifica-<lb>tion consists in allowing the method adaptively<lb>change the probability distribution over the dual<lb>variables throughout the iterative process. AdaS-<lb>DCA achieves provably better complexity bound<lb>than SDCA with the best fixed probability dis-<lb>tribution, known as importance sampling. How-<lb>ever, it is of a theoretical character as it is expen-<lb>sive to implement. We also propose AdaSDCA+:<lb>a practical variant which in our experiments out-<lb>performs existing non-adaptive methods.
Example Output:
Stochastic Dual Coordinate Ascent with Adaptive Probabilities