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.
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Question: Recently there has been an increasing trend to use deep learning frameworks for both 2D consumer images and for 3D medical images. However, there has been little effort to use deep frameworks for volumetric vascular segmentation. We wanted to address this by providing a freely available dataset of 12 annotated two-photon vasculature microscopy stacks. We demonstrated the use of deep learning framework consisting both 2D and 3D convolutional filters (ConvNet). Our hybrid 2D-3D architecture produced promising segmentation result. We derived the architectures from Lee et al. who used the ZNN framework initially designed for electron microscope image segmentation. We hope that by sharing our volumetric vasculature datasets, we will inspire other researchers to experiment with vasculature dataset and improve the used network architectures.

Answer: Deep Learning Convolutional Networks for Multiphoton Microscopy Vasculature Segmentation


Question: Multimedia or spoken content presents more attractive information than plain text content, but the former is more difficult to display on a screen and be selected by a user. As a result, accessing large collections of the former is much more difficult and time-consuming than the latter for humans. It’s therefore highly attractive to develop machines which can automatically understand spoken content and summarize the key information for humans to browse over. In this endeavor, a new task of machine comprehension of spoken content was proposed recently. The initial goal was defined as the listening comprehension test of TOEFL, a challenging academic English examination for English learners whose native languages are not English. An Attention-based Multi-hop Recurrent Neural Network (AMRNN) architecture was also proposed for this task, which considered only the sequential relationship within the speech utterances. In this paper, we propose a new Hierarchical Attention Model (HAM), which constructs multi-hopped attention mechanism over tree-structured rather than sequential representations for the utterances. Improved comprehension performance robust with respect to ASR errors were obtained.

Answer: HIERARCHICAL ATTENTION MODEL FOR IMPROVED MACHINE COMPREHENSION OF SPOKEN CONTENT


Question: We propose an efficient technique for multilabel classification based on calibration, a term we use to mean learning a link function that maps independent predictions to joint predictions. Though a naive implementation of our proposal would require training individual classifiers for each label, we show that for natural datasets and linear classifiers we can sidestep this by leveraging techniques from randomized linear algebra. Moreover, our algorithm applies equally well to multiclass classification. The end result is an algorithm that scales to very large multilabel and multiclass problems, and offers state of the art accuracy on many datasets.

Answer:
Multilabel Prediction via Calibration