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: Most existing Neural Machine Translation models use groups of characters or whole words as their unit of input and output. We propose a model with a hierarchical char2word encoder, that takes individual characters both as input and output. We first argue that this hierarchical representation of the character encoder reduces computational complexity, and show that it improves translation performance. Secondly, by qualitatively studying attention plots from the decoder we find that the model learns to compress common words into a single embedding whereas rare words, such as names and places, are represented character by character.

A: NEURAL MACHINE TRANSLATION WITH CHARACTERS AND HIERARCHICAL ENCODING
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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
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Q: A machine learning method needs to adapt to over time changes in the environment. Such changes are known as concept drift. One approach to concept drift handling is by feeding the whole training data set once again into a learning machine for retraining. Another approach is by rebuilding an ensemble classifiers to adapt to a new training data set. In either approach, retraining or rebuilding classifiers are expensive and not practical. In this paper, we propose an enhancement of Online-Sequential Extreme Learning Machine (OS-ELM) and its variant Constructive Enhancement OS-ELM (CEOS-ELM) by adding an adaptive capability for classification and regression problem. The scheme is named as Adaptive OS-ELM (AOS-ELM). It is a single classifier scheme that works well to handle real drift, virtual drift, and both drifts occurred at the same time (hybrid drift). The AOS-ELM also works well for sudden drift as well as recurrent context change type. The scheme is a simple unified method implemented in simple lines of code. We evaluated AOS-ELM on regression and classification problem by using various public dataset widely used for concept drift verification from SEA and STAGGER; and other public datasets such as MNIST and USPS. Experiments show that our method gives higher kappa value compared to the multi-classifier ELM ensemble. Even though AOS-ELM in practice does not need hidden nodes increase, we address some issues related to the increasing of the hidden nodes such as error condition and rank values. We propose to take the rank of the pseudo inverse matrix as an indicator parameter to detect ’under-fitting’ condition. Keywords— adaptive, concept drift, extreme learning machine, online sequential.

A:
Adaptive Online Sequential ELM for Concept Drift Tackling
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