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 Input:
Topic models provide a useful method for dimensionality reduction and exploratory data<lb>analysis in large text corpora. Most approaches to topic model inference have been based on<lb>a maximum likelihood objective. Efficient algorithms exist that approximate this objective,<lb>but they have no provable guarantees. Recently, algorithms have been introduced that provide<lb>provable bounds, but these algorithms are not practical because they are inefficient and not ro-<lb>bust to violations of model assumptions. In this paper we present an algorithm for topic model<lb>inference that is both provable and practical. The algorithm produces results comparable to the<lb>best MCMC implementations while running orders of magnitude faster.

Ex Output:
A Practical Algorithm for Topic Modeling with Provable Guarantees


Ex Input:
A currently successful approach to computational semantics is to represent words as embeddings in a machine-learned vector space. We present an ensemble method that combines embeddings produced by GloVe (Pennington et al., 2014) and word2vec (Mikolov et al., 2013) with structured knowledge from the semantic networks ConceptNet (Speer and Havasi, 2012) and PPDB (Ganitkevitch et al., 2013), merging their information into a common representation with a large, multilingual vocabulary. The embeddings it produces achieve state-of-the-art performance on many word-similarity evaluations. Its score of ρ = .596 on an evaluation of rare words (Luong et al., 2013) is 16% higher than the previous best known system.

Ex Output:
An Ensemble Method to Produce High-Quality Word Embeddings


Ex Input:
The ability to simultaneously leverage multiple modes of sensor information is critical for perception of an automated vehicle’s physical surroundings. Spatio-temporal alignment of registration of the incoming information is often a prerequisite to analyzing the fused data. The persistence and reliability of multi-modal registration is therefore the key to the stability of decision support systems ingesting the fused information. LiDAR-video systems like on those many driverless cars are a common example of where keeping the LiDAR and video channels registered to common physical features is important. We develop a deep learning method that takes multiple channels of heterogeneous data, to detect the misalignment of the LiDARvideo inputs. A number of variations were tested on the Ford LiDAR-video driving test data set and will be discussed. To the best of our knowledge the use of multi-modal deep convolutional neural networks for dynamic real-time LiDAR-video registration has not been presented.

Ex Output:

Multi-modal Sensor Registration for Vehicle Perception via Deep Neural Networks