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.

[Q]: 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.
[A]: A Practical Algorithm for Topic Modeling with Provable Guarantees

[Q]: We propose a novel deep layer cascade (LC) method to improve the accuracy and speed of semantic segmentation. Unlike the conventional model cascade (MC) that is composed of multiple independent models, LC treats a single deep model as a cascade of several sub-models. Earlier sub-models are trained to handle easy and confident regions, and they progressively feed-forward harder regions to the next sub-model for processing. Convolutions are only calculated on these regions to reduce computations. The proposed method possesses several advantages. First, LC classifies most of the easy regions in the shallow stage and makes deeper stage focuses on a few hard regions. Such an adaptive and 'difficulty-aware' learning improves segmentation performance. Second, LC accelerates both training and testing of deep network thanks to early decisions in the shallow stage. Third, in comparison to MC, LC is an endto-end trainable framework, allowing joint learning of all sub-models. We evaluate our method on PASCAL VOC and Cityscapes datasets, achieving state-of-the-art performance and fast speed.
[A]: Not All Pixels Are Equal: Difficulty-Aware Semantic Segmentation via Deep Layer Cascade

[Q]: Basics 5 Learning Tasks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Task types and creation . . . . . . . . . . . . . . . . . . . . . . . 5 Further settings . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Accessing a learning task . . . . . . . . . . . . . . . . . . . . . . 10 Modifying a learning task . . . . . . . . . . . . . . . . . . . . . . 13 Example tasks and convenience functions . . . . . . . . . . . . . 15 Learners . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Constructing a learner . . . . . . . . . . . . . . . . . . . . . . . . 15 Accessing a learner . . . . . . . . . . . . . . . . . . . . . . . . . . 17 Modifying a learner . . . . . . . . . . . . . . . . . . . . . . . . . . 20 Listing learners . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 Training a Learner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 Accessing learner models . . . . . . . . . . . . . . . . . . . . . . . 23 Further options and comments . . . . . . . . . . . . . . . . . . . 26 Predicting Outcomes for New Data . . . . . . . . . . . . . . . . . . . . 27 Accessing the prediction . . . . . . . . . . . . . . . . . . . . . . . 29 Adjusting the threshold . . . . . . . . . . . . . . . . . . . . . . . 33 Visualizing the prediction . . . . . . . . . . . . . . . . . . . . . . 35 Evaluating Learner Performance . . . . . . . . . . . . . . . . . . . . . 38 Available performance measures . . . . . . . . . . . . . . . . . . . 38 Listing measures . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 Calculate performance measures . . . . . . . . . . . . . . . . . . 40 Access a performance measure . . . . . . . . . . . . . . . . . . . . 41 Binary classification . . . . . . . . . . . . . . . . . . . . . . . . . 42 Resampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 Stratified resampling . . . . . . . . . . . . . . . . . . . . . . . . . 47
[A]:
mlr Tutorial