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.

[EX Q]: This paper presents to the best of our knowledge the first end-to-end object tracking approach which directly maps from raw sensor input to object tracks in sensor space without requiring any feature engineering or system identification in the form of plant or sensor models. Specifically, our system accepts a stream of raw sensor data at one end and, in real-time, produces an estimate of the entire environment state at the output including even occluded objects. We achieve this by framing the problem as a deep learning task and exploit sequence models in the form of recurrent neural networks to learn a mapping from sensor measurements to object tracks. In particular, we propose a learning method based on a form of input dropout which allows learning in an unsupervised manner, only based on raw, occluded sensor data without access to ground-truth annotations. We demonstrate our approach using a synthetic dataset designed to mimic the task of tracking objects in 2D laser data – as commonly encountered in robotics applications – and show that it learns to track many dynamic objects despite occlusions and the presence of sensor noise.
[EX A]: Deep Tracking: Seeing Beyond Seeing Using Recurrent Neural Networks

[EX Q]: In this paper, we will expound upon the concepts proffered in [5], where we proposed an information theoretic approach to intelligence in the computational sense. We will examine data and meme aggregation, and study the effect of limited resources on the resulting meme amplitudes.
[EX A]: The Computational Theory of Intelligence: Data Aggregation

[EX Q]: We tackle the problem of inferring node labels in a partially labeled graph where each node in the graph has multiple label types and each label type has a large number of possible labels. Our primary example, and the focus of this paper, is the joint inference of label types such as hometown, current city, and employers, for users connected by a social network. Standard label propagation fails to consider the properties of the label types and the interactions between them. Our proposed method, called EDGEEXPLAIN, explicitly models these, while still enabling scalable inference under a distributed message-passing architecture. On a billion-node subset of the Facebook social network, EDGEEXPLAIN significantly outperforms label propagation for several label types, with lifts of up to 120% for recall@1 and 60% for recall@3.
[EX A]:
Joint Inference of Multiple Label Types in Large Networks