instruction:
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.
question:
Despite progress in visual perception tasks such as image classification and detection, computers still struggle to understand the interdependency of objects in the scene as a whole, e.g., relations between objects or their attributes. Existing methods often ignore global context cues capturing the interactions among different object instances, and can only recognize a handful of types by exhaustively training individual detectors for all possible relationships. To capture such global interdependency, we propose a deep Variation-structured Reinforcement Learning (VRL) framework to sequentially discover object relationships and attributes in the whole image. First, a directed semantic action graph is built using language priors to provide a rich and compact representation of semantic correlations between object categories, predicates, and attributes. Next, we use a variation-structured traversal over the action graph to construct a small, adaptive action set for each step based on the current state and historical actions. In particular, an ambiguity-aware object mining scheme is used to resolve semantic ambiguity among object categories that the object detector fails to distinguish. We then make sequential predictions using a deep RL framework, incorporating global context cues and semantic embeddings of previously extracted phrases in the state vector. Our experiments on the Visual Relationship Detection (VRD) dataset and the large-scale Visual Genome dataset validate the superiority of VRL, which can achieve significantly better detection results on datasets involving thousands of relationship and attribute types. We also demonstrate that VRL is able to predict unseen types embedded in our action graph by learning correlations on shared graph nodes.
answer:
Deep Variation-structured Reinforcement Learning for Visual Relationship and Attribute Detection


question:
Levels are a key component of many different video games, and a large body of work has been produced on how to procedurally generate game levels. Recently, Machine Learning techniques have been applied to video game level generation towards the purpose of automatically generating levels that have the properties of the training corpus. Towards that end we have made available a corpora of video game levels in an easy to parse format ideal for different machine learning and other game AI research purposes.
answer:
The VGLC: The Video Game Level Corpus


question:
<lb>This work addresses the problem of regret minimization in non-stochastic multi-<lb>armed bandit problems, focusing on performance guarantees that hold with high<lb>probability. Such results are rather scarce in the literature since proving them re-<lb>quires a large deal of technical effort and significant modifications to the standard,<lb>more intuitive algorithms that come only with guarantees that hold on expectation.<lb>One of these modifications is forcing the learner to sample arms from the uniform<lb>distribution at least Ω(<lb>√<lb>T ) times over T rounds, which can adversely affect per-<lb>formance if many of the arms are suboptimal. While it is widely conjectured that<lb>this property is essential for proving high-probability regret bounds, we show in<lb>this paper that it is possible to achieve such strong results without this undesirable<lb>exploration component. Our result relies on a simple and intuitive loss-estimation<lb>strategy called Implicit eXploration (IX) that allows a remarkably clean analy-<lb>sis. To demonstrate the flexibility of our technique, we derive several improved<lb>high-probability bounds for various extensions of the standard multi-armed bandit<lb>framework. Finally, we conduct a simple experiment that illustrates the robustness<lb>of our implicit exploration technique.
answer:

Explore no more: Improved high-probability regret bounds for non-stochastic bandits