instruction:
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.
question:
Medical errors are leading causes of death in the US and as such, prevention of these errors is paramount to promoting healthcare. Patient Safety Event reports are narratives describing potential adverse events to the patients and are important in identifying, and preventing medical errors. We present a neural network architecture for identifying the type of safety events which is the first step in understanding these narratives. Our proposed model is based on a soft neural attention model to improve the effectiveness of encoding long sequences. Empirical results on two large-scale real-world datasets of patient safety reports demonstrate the effectiveness of our method with significant improvements over existing methods.
answer:
A Neural Attention Model for Categorizing Patient Safety Events


question:
Classes in natural images tend to follow long tail distributions. This is problematic when there are insufficient training examples for rare classes. This effect is emphasized in compound classes, involving the conjunction of several concepts, such as those appearing in action-recognition datasets. In this paper, we propose to address this issue by learning how to utilize common visual concepts which are readily available. We detect the presence of prominent concepts in images and use them to infer the target labels instead of using visual features directly, combining tools from vision and natural-language processing. We validate our method on the recently introduced HICO dataset reaching a mAP of 31.54% and on the Stanford40 Actions dataset, where the proposed method outperforms current state-of-the art and, combined with direct visual features, obtains an accuracy 83.12%. Moreover, the method provides for each class a semantically meaningful list of keywords and relevant image regions relating it to its constituent concepts.
answer:
Action Classification via Concepts and Attributes


question:
Deep neural networks have dramatically advanced the state of the art for many<lb>areas of machine learning. Recently they have been shown to have a remarkable<lb>ability to generate highly complex visual artifacts such as images and text rather<lb>than simply recognize them.<lb>In this work we use neural networks to effectively invert low-dimensional face<lb>embeddings while producing realistically looking consistent images. Our contri-<lb>bution is twofold, first we show that a gradient ascent style approaches can be<lb>used to reproduce consistent images, with a help of a guiding image. Second, we<lb>demonstrate that we can train a separate neural network to effectively solve the<lb>minimization problem in one pass, and generate images in real-time. We then<lb>evaluate the loss imposed by using a neural network instead of the gradient descent<lb>by comparing the final values of the minimized loss function.
answer:

Inverting face embeddings with convolutional neural networks