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 Q]: Visual representation is crucial for a visual tracking method’s performances. Conventionally, visual representations adopted in visual tracking rely on hand-crafted computer vision descriptors. These descriptors were developed generically without considering tracking-specific information. In this paper, we propose to learn complex-valued invariant representations from tracked sequential image patches, via strong temporal slowness constraint and stacked convolutional autoencoders. The deep slow local representations are learned offline on unlabeled data and transferred to the observational model of our proposed tracker. The proposed observational model retains old training samples to alleviate drift, and collect negative samples which are coherent with target’s motion pattern for better discriminative tracking. With the learned representation and online training samples, a logistic regression classifier is adopted to distinguish target from background, and retrained online to adapt to appearance changes. Subsequently, the observational model is integrated into a particle filter framework to peform visual tracking. Experimental results on various challenging benchmark sequences demonstrate that the proposed tracker performs favourably against several state-of-the-art trackers.
[EX A]: Self-taught learning of a deep invariant representation for visual tracking via temporal slowness principle

[EX Q]: Understanding entailment and contradiction is fundamental to understanding natural language, and inference about entailment and contradiction is a valuable testing ground for the development of semantic representations. However, machine learning research in this area has been dramatically limited by the lack of large-scale resources. To address this, we introduce the Stanford Natural Language Inference corpus, a new, freely available collection of labeled sentence pairs, written by humans doing a novel grounded task based on image captioning. At 570K pairs, it is two orders of magnitude larger than all other resources of its type. This increase in scale allows lexicalized classifiers to outperform some sophisticated existing entailment models, and it allows a neural network-based model to perform competitively on natural language inference benchmarks for the first time.
[EX A]: A large annotated corpus for learning natural language inference

[EX Q]: Transferring knowledge across a sequence of related tasks is an important challenge in reinforce-<lb>ment learning. Despite much encouraging empirical evidence that shows benefits of transfer, there<lb>has been very little theoretical analysis. In this paper, we study a class of lifelong reinforcement-<lb>learning problems: the agent solves a sequence of tasks modeled as finite Markov decision processes<lb>(MDPs), each of which is from a finite set of MDPs with the same state/action spaces and different<lb>transition/reward functions. Inspired by the need for cross-task exploration in lifelong learning, we<lb>formulate a novel online discovery problem and give an optimal learning algorithm to solve it. Such<lb>results allow us to develop a new lifelong reinforcement-learning algorithm, whose overall sample<lb>complexity in a sequence of tasks is much smaller than that of single-task learning, with high prob-<lb>ability, even if the sequence of tasks is generated by an adversary. Benefits of the algorithm are<lb>demonstrated in a simulated problem.
[EX A]:
The Online Discovery Problem and Its Application to Lifelong Reinforcement Learning