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: Robot warehouse automation has attracted significant interest in recent years, perhaps most visibly in the Amazon Picking Challenge (APC) [1]. A fully autonomous warehouse pick-and-place system requires robust vision that reliably recognizes and locates objects amid cluttered environments, self-occlusions, sensor noise, and a large variety of objects. In this paper we present an approach that leverages multiview RGB-D data and self-supervised, data-driven learning to overcome those difficulties. The approach was part of the MITPrinceton Team system that took 3rdand 4thplace in the stowing and picking tasks, respectively at APC 2016. In the proposed approach, we segment and label multiple views of a scene with a fully convolutional neural network, and then fit pre-scanned 3D object models to the resulting segmentation to get the 6D object pose. Training a deep neural network for segmentation typically requires a large amount of training data. We propose a self-supervised method to generate a large labeled dataset without tedious manual segmentation. We demonstrate that our system can reliably estimate the 6D pose of objects under a variety of scenarios. All code, data, and benchmarks are available at http://www.cs.princeton.edu/∼andyz/apc2016.
Example Output: Multi-view Self-supervised Deep Learning for 6D Pose Estimation in the Amazon Picking Challenge

Example Input: Understanding a long document requires tracking how entities are introduced and evolve over time. We present a new type of language model, ENTITYNLM, that can explicitly model entities, dynamically update their representations, and contextually generate their mentions. Our model is generative and flexible; it can model an arbitrary number of entities in context while generating each entity mention at an arbitrary length. In addition, it can be used for several different tasks such as language modeling, coreference resolution, and entity prediction. Experimental results with all these tasks demonstrate that our model consistently outperforms strong baselines and prior work.
Example Output: Dynamic Entity Representations in Neural Language Models

Example Input: In this paper, we use evidence-specific value ab­ straction for speeding Bayesian networks infer­ ence. This is done by grouping variable val­ ues and treating the combined values as a sin­ gle entity. As we show, such abstractions can ex­ ploit regularities in conditional probability distri­ butions and also the specific values of observed variables. To formally justify value abstraction, we defi ne the notion of safe value abstraction and devise inference algorithms that use it to re­ duce the cost of inference. Our procedure is par­ ticularly useful for learning complex networks with many hidden variables. In such cases, re­ peated likelihood computations are required for E M or other parameter optimization techniques. Since these computations are repeated with re­ spect to the same evidence set, our methods can provide signifi cant speedup to the learning pro­ cedure. We demonstrate the algorithm on genetic linkage problems where the use of value abstrac­ tion sometimes differentiates between a feasible and non-feasible solution.
Example Output:
Likelihood Computations Using Value Abstraction