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: 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.

Answer: Deep Tracking: Seeing Beyond Seeing Using Recurrent Neural Networks


Question: We develop a worst-case analysis of aggregation of binary classifier ensembles in a transductive setting, for a broad class of losses including but not limited to all convex surrogates. The result is a family of parameter-free ensemble aggregation algorithms, which are as efficient as linear learning and prediction for convex risk minimization but work without any relaxations whatsoever on many nonconvex losses like the 0-1 loss. The prediction algorithms take a familiar form, applying “link functions" to a generalized notion of ensemble margin, but without the assumptions typically made in margin-based learning – all this structure follows from a minimax interpretation of loss minimization.

Answer: Minimax Binary Classifier Aggregation with General Losses


Question: Logic programs with aggregates (LP) are one of the major linguistic extensions to Logic Programming (LP). In this work, we propose a generalization of the notions of unfounded set and well-founded semantics for programs with monotone and antimonotone aggregates (LPm,a programs). In particular, we present a new notion of unfounded set for LPm,a programs, which is a sound generalization of the original definition for standard (aggregate-free) LP. On this basis, we define a well-founded operator for LPm,a programs, the fixpoint of which is called well-founded model (or well-founded semantics) for LPm,a programs. The most important properties of unfounded sets and the well-founded semantics for standard LP are retained by this generalization, notably existence and uniqueness of the well-founded model, together with a strong relationship to the answer set semantics for LPm,a programs. We show that one of the D̃-well-founded semantics, defined by Pelov, Denecker, and Bruynooghe for a broader class of aggregates using approximating operators, coincides with the well-founded model as defined in this work on LPm,a programs. We also discuss some complexity issues, most importantly we give a formal proof of tractable computation of the well-founded model for LPm,a programs. Moreover, we prove that for general LP programs, which may contain aggregates that are neither monotone nor antimonotone, deciding satisfaction of aggregate expressions with respect to partial interpretations is coNP-complete. As a consequence, a well-founded semantics for general LP programs that allows for tractable computation is unlikely to exist, which justifies the restriction on LPm,a programs. Finally, we present a prototype system extending DLV, which supports the well-founded semantics for LPm,a programs, at the time of writing the only implemented system that does so. Experiments with this prototype show significant computational advantages of aggregate constructs over equivalent aggregate-free encodings.

Answer:
Unfounded Sets and Well-Founded Semantics of Answer Set Programs with Aggregates